Estimation of Cancer Deaths Averted From Prevention, Screening, and Treatment Efforts, 1975-2020
Bibliographic record
Abstract
Importance: Cancer mortality has decreased over time, but the contributions of different interventions across the cancer control continuum to averting cancer deaths have not been systematically evaluated across major cancer sites. Objective: To quantify the contributions of prevention, screening (to remove precursors [interception] or early detection), and treatment to cumulative number of cancer deaths averted from 1975 to 2020 for breast, cervical, colorectal, lung, and prostate cancers. Design, Setting, and Participants: In this model-based study using population-level cancer mortality data, outputs from published models developed by the Cancer Intervention and Surveillance Modeling Network were extended to quantify cancer deaths averted through 2020. Model inputs were based on national data on risk factors, cancer incidence, cancer survival, and mortality due to other causes, and dissemination and effects of prevention, screening (for interception and early detection), and treatment. Simulated or modeled data using parameters derived from multiple birth cohorts of the US population were used. Interventions: Primary prevention via smoking reduction (lung), screening for interception (cervix and colorectal) or early detection (breast, cervix, colorectal, and prostate), and therapy (breast, colorectal, lung, and prostate). Main Outcomes and Measures: The estimated cumulative number of cancer deaths averted with interventions vs no advances. Results: An estimated 5.94 million cancer deaths were averted for breast, cervical, colorectal, lung, and prostate cancers combined. Cancer prevention and screening efforts averted 8 of 10 of these deaths (4.75 million averted deaths). The contribution of each intervention varied by cancer site. Screening accounted for 25% of breast cancer deaths averted. Averted cervical cancer deaths were nearly completely averted through screening and removal of cancer precursors as treatment advances were modest during the study period. Averted colorectal cancer deaths were averted because of screening and removal of precancerous polyps or early detection in 79% and treatment advances in 21%. Most lung cancer deaths were avoided by smoking reduction (98%) because screening uptake was low and treatment largely palliative before 2014. Screening contributed to 56% of averted prostate cancer deaths. Conclusions and Relevance: Over the past 45 years, cancer prevention and screening accounted for most cancer deaths averted for these causes; however, their contribution varied by cancer site according to these models using population-level cancer mortality data. Despite progress, efforts to reduce the US cancer burden will require increased dissemination of effective interventions and new technologies and discoveries.
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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".