Cancers Attributable to Infections in the US in 2017
Bibliographic record
Abstract
Importance: Infections are largely modifiable causes of cancer. However, there remains untapped potential for preventing and treating carcinogenic infections in the US. Objective: To estimate the percentage and number of incident cancers attributable to infections in the US among adults and children for the most recent year cancer incidence data were available (2017). Data Sources: A literature search from 1946 onward was performed in MEDLINE on January 6, 2023, to obtain the data required to calculate population attributable fractions for 31 infection-cancer pairs. National Health and Nutrition Examination Survey data were used to estimate the population prevalence of hepatitis B and C viruses and Helicobacter pylori. Study Selection: Studies conducted in the US or other Western countries were selected according to specific infection-cancer criteria. Data Extraction and Synthesis: Data from 128 studies were meta-analyzed to obtain the magnitude of an infection-cancer association or prevalence of the infection within cancer cells. Main Outcomes and Measures: The proportion of cancer incidence attributable to 8 infections. Results: Of the 1 666 102 cancers diagnosed in 2017 among individuals aged 20 years or older in the US, 71 485 (4.3%; 95% CI, 3.1%-5.3%) were attributable to infections. Human papillomavirus (n = 38 230) was responsible for the most cancers, followed by H pylori (n = 10 624), hepatitis C virus (n = 9006), Epstein-Barr virus (n = 7581), hepatitis B virus (n = 2310), Merkel cell polyomavirus (n = 2000), Kaposi sarcoma-associated herpesvirus (n = 1075), and human T-cell lymphotropic virus type 1 (n = 659). Cancers with the most infection-attributable cases were cervical (human papillomavirus; n = 12 829), gastric (H pylori and Epstein-Barr virus; n = 12 565), oropharynx (human papillomavirus; n = 12 430), and hepatocellular carcinoma (hepatitis B and C viruses; n = 10 017). The burden of infection-attributable cancers as a proportion of total cancer incidence ranged from 9.6% (95% CI, 9.2%-10.0%) for women aged 20 to 34 years to 3.2% (95% CI, 2.4%-3.8%) for women aged 65 years or older and from 6.1% (95% CI, 5.2%-7.0%) for men aged 20 to 34 years to 3.3% (95% CI, 1.9%-4.4%) for men aged 65 years or older. Among those aged 19 years or younger, 2.2% (95% CI, 1.3%-3.0%) of cancers diagnosed in 2017 were attributable to Epstein-Barr virus. Conclusions and Relevance: Infections were estimated to be responsible for 4.3% of cancers diagnosed among adults in the US in 2017 and, therefore, represent an important target for cancer prevention efforts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 0.001 |
| 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".