Estimating the Proportion of Overdiagnosis among Prostate, Breast, and Thyroid Cancers in China: Findings from the Global Burden of Disease 2019
Bibliographic record
Abstract
The incidence of prostate, breast, and thyroid cancers has increased in China over the past few decades. Whether and how much these increases can be attributed to overdiagnosis are less understood. This study aimed to estimate the proportion of overdiagnosis among these three cancers in China during 2004–2019. The age-specific cancer incidence, cancer mortality, and all-cause mortality in China were extracted from the Global Burden of Diseases 2019. The lifetime risk of developing and that of dying from each cancer were calculated using the life table method. The proportion of overdiagnosis of a cancer was estimated as the difference between the lifetime risk of developing the cancer and that of suffering from the cancer (including death, metastasis, and symptoms caused by the cancer), further divided by the lifetime risk of developing the cancer. The highest possible values of these parameters were adopted in the estimation so as to obtain the lower bounds of the proportions of overdiagnosis. Sensitivity analyses assuming different lag periods between the diagnosis of a cancer and death from the cancer were performed. The results showed that the lifetime risk of developing prostate, breast, and thyroid cancer increased dramatically from 2004 to 2019 in China, while the increase in the lifetime risk of dying from these cancers was less pronounced. The proportions of overdiagnosis among prostate, breast, and thyroid cancers were estimated to be 7.88%, 18.99%, and 24.92%, respectively, in 2004, and increased to 18.20%, 26.25%, and 29.24%, respectively, in 2019. The increasing trends were statistically significant for all three cancers (all p < 0.001). In sensitivity analyses, the proportions of overdiagnosis decreased, but upward trends over time remained for all three cancers. In conclusion, the overdiagnosis of prostate, breast, and thyroid cancers in China increased from 2004 to 2019, with the highest proportion seen in thyroid cancer and the most rapid increase seen in prostate cancer. Multifaceted efforts by policy makers, guideline developers, and clinicians are needed to tackle this problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".