Our work involves assessing whether new biomarkers might be useful for cervical-cancer screening across populations with different disease prevalences and biomarker distributions. When comparing ...across populations, we show that standard diagnostic accuracy statistics (predictive values, risk-differences, Youden's index and Area Under the Curve (AUC)) can easily be misinterpreted. We introduce an intuitively simple statistic for a 2 × 2 table, Mean Risk Stratification (MRS): the average change in risk (pre-test vs. post-test) revealed for tested individuals. High MRS implies better risk separation achieved by testing. MRS has 3 key advantages for comparing test performance across populations with different disease prevalences and biomarker distributions. First, MRS demonstrates that conventional predictive values and the risk-difference do not measure risk-stratification because they do not account for test-positivity rates. Second, Youden's index and AUC measure only multiplicative relative gains in risk-stratification: AUC = 0.6 achieves only 20% of maximum risk-stratification (AUC = 0.9 achieves 80%). Third, large relative gains in risk-stratification might not imply large absolute gains if disease is rare, demonstrating a “high-bar” to justify population-based screening for rare diseases such as cancer. We illustrate MRS by our experience comparing the performance of cervical-cancer screening tests in China vs. the USA. The test with the worst AUC = 0.72 in China (visual inspection with acetic acid) provides twice the risk-stratification (i.e. MRS) of the test with best AUC = 0.83 in the USA (human papillomavirus and Pap cotesting) because China has three times more cervical precancer/cancer. MRS could be routinely calculated to better understand the clinical/public-health implications of standard diagnostic accuracy statistics.
•We propose new diagnostic accuracy metrics for comparing tests across populations.•Rare diseases inherently permit limited risk-stratification.•Tests that are rarely positive do not provide much risk-stratification.•High AUC does not imply good risk-stratification if the disease is too rare.•Our new metrics support a ‘high-bar’ to justify population screening for cancer.
Human papillomavirus (HPV) infection, particularly with type 16, causes a growing fraction of oropharyngeal cancers, whose incidence is increasing, mainly in developed countries. In a double-blind ...controlled trial conducted to investigate vaccine efficacy (VE) of the bivalent HPV 16/18 vaccine against cervical infections and lesions, we estimated VE against prevalent oral HPV infections 4 years after vaccination.
A total of 7,466 women 18-25 years old were randomized (1∶1) to receive the HPV16/18 vaccine or hepatitis A vaccine as control. At the final blinded 4-year study visit, 5,840 participants provided oral specimens (91·9% of eligible women) to evaluate VE against oral infections. Our primary analysis evaluated prevalent oral HPV infection among all vaccinated women with oral and cervical HPV results. Corresponding VE against prevalent cervical HPV16/18 infection was calculated for comparison. Oral prevalence of identifiable mucosal HPV was relatively low (1·7%). Approximately four years after vaccination, there were 15 prevalent HPV16/18 infections in the control group and one in the vaccine group, for an estimated VE of 93·3% (95% CI = 63% to 100%). Corresponding efficacy against prevalent cervical HPV16/18 infection for the same cohort at the same visit was 72·0% (95% CI = 63% to 79%) (p versus oral VE = 0·04). There was no statistically significant protection against other oral HPV infections, though power was limited for these analyses.
HPV prevalence four years after vaccination with the ASO4-adjuvanted HPV16/18 vaccine was much lower among women in the vaccine arm compared to the control arm, suggesting that the vaccine affords strong protection against oral HPV16/18 infection, with potentially important implications for prevention of increasingly common HPV-associated oropharyngeal cancer. ClinicalTrials.gov, Registry number NCT00128661.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Multi-cancer tests offer screening for multiple cancers with one blood draw, but the potential population impact is poorly understood.
We formulate mathematical expressions for expected numbers of: ...(i) individuals exposed to unnecessary confirmation tests (EUC), (ii) cancers detected (CD), and (iii) lives saved (LS) given test performance, disease incidence and mortality, and mortality reduction. We add colorectal, liver, lung, ovary, and pancreatic cancer to a test for breast cancer, approximating prevalence at ages 50, 60, or 70 using incidence over the next 5 years and mortality using corresponding probabilities of cancer death over 15 years in the Surveillance, Epidemiology, and End Results registry.
EUC is overwhelmingly determined by specificity. For a given specificity, EUC/CD is most favorable for higher prevalence cancers. Under 99% specificity and sensitivities as published for a 50-cancer test, EUC/CD is 1.1 for breast + lung versus 1.3 for breast + liver at age 50. Under a common mortality reduction associated with screening, EUC/LS> is most favorable when the test includes higher mortality cancers (e.g., 19.9 for breast + lung vs. 30.4 for breast + liver at age 50 assuming a common 10% mortality reduction).
Published multi-cancer test performance suggests a favorable tradeoff of EUC to CD, yet the full burden of unnecessary confirmations will depend on the posttest work-up protocol. Harm-benefit tradeoffs will be improved if tests prioritize more prevalent and/or lethal cancers for which curative treatments exist.
The population impact of multi-cancer testing will depend not only on test performance but also on disease characteristics and efficacy of early treatment.
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Abstract
Objective
The US Preventive Services Task Force (USPSTF) requires the estimation of lifetime pack-years to determine lung cancer screening eligibility. Leading electronic health record (EHR) ...vendors calculate pack-years using only the most recently recorded smoking data. The objective was to characterize EHR smoking data issues and to propose an approach to addressing these issues using longitudinal smoking data.
Materials and Methods
In this cross-sectional study, we evaluated 16 874 current or former smokers who met USPSTF age criteria for screening (50–80 years old), had no prior lung cancer diagnosis, and were seen in 2020 at an academic health system using the Epic® EHR. We described and quantified issues in the smoking data. We then estimated how many additional potentially eligible patients could be identified using longitudinal data. The approach was verified through manual review of records from 100 subjects.
Results
Over 80% of evaluated records had inaccuracies, including missing packs-per-day or years-smoked (42.7%), outdated data (25.1%), missing years-quit (17.4%), and a recent change in packs-per-day resulting in inaccurate lifetime pack-years estimation (16.9%). Addressing these issues by using longitudinal data enabled the identification of 49.4% more patients potentially eligible for lung cancer screening (P < .001).
Discussion
Missing, outdated, and inaccurate smoking data in the EHR are important barriers to effective lung cancer screening. Data collection and analysis strategies that reflect changes in smoking habits over time could improve the identification of patients eligible for screening.
Conclusion
The use of longitudinal EHR smoking data could improve lung cancer screening.
OBJECTIVE:HIV-infected people and elderly people have higher cancer risk, but the combined effects of aging and HIV are not well described. We aimed to evaluate the magnitude of cancer risk in the ...HIV-infected elderly population.
DESIGN:We conducted a case-cohort study including a 5% sample of U.S. Medicare enrollees and all cancer cases aged at least 65 in linked cancer registries.
METHODS:HIV was identified through Medicare claims. Among the HIV-infected, absolute cancer risk was calculated accounting for the competing risk of death. Associations between HIV and cancer were estimated with weighted Cox regression adjusting for demographic characteristics.
RESULTS:Among 469 954 people in the 5% sample, 0.08% had an HIV diagnosis. Overall, 825 776 cancer cases were identified in cancer registries. Over 5 years, 10.1% of the HIV-infected elderly developed cancer, the most common diagnoses comprising lung (5-year cumulative incidence=2.2%), prostate (2.7%, among men), and colorectal cancer (0.9%), and non-Hodgkin lymphoma (0.8%). HIV was strongly associated with incidence of Kaposi sarcoma adjusted hazard ratio (aHR)=94.4, 95% confidence interval (95%CI)=54.6–163, anal cancer (aHR=34.2, 95%CI=23.9–49.0) and Hodgkin lymphoma (aHR=6.3, 95%CI=2.8–14.3). HIV was also associated with incidence of liver cancer, non-Hodgkin lymphoma and lung cancer (aHR=3.4, 2.6, and 1.6, respectively).
CONCLUSION:In the elderly, HIV infection is associated with higher risk for many cancers, although some associations were weaker than expected, perhaps reflecting effects of non-HIV pathways on cancer development. Due to the effects of HIV and aging, the HIV-infected elderly have a sizeable absolute risk, highlighting a need for cancer prevention.
Background
This report quantifies counteracting effects of quit‐years and concomitant aging on lung cancer risk, especially on exceeding 15 quit‐years, when the US Preventive Services Task Force ...(USPSTF) recommends curtailing lung‐cancer screening.
Methods
Cox models were fitted to estimate absolute lung cancer risk among Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) and National Lung Screening Trial (NLST) participants who ever smoked. Absolute lung cancer risk and gainable years of life from screening for individuals aged 50 to 80 in the US‐representative National Health Interview Survey (NHIS) 2015–2018 who ever smoked were projected. Relaxing USPSTF recommendations to 20/25/30 quit‐years versus augmenting USPSTF criteria with individuals whose estimated gain in life expectancy from screening exceeded 16.2 days according to the Life Years From Screening‐CT (LYFS‐CT) prediction model was compared.
Results
Absolute lung cancer risk increased by 8.7%/year (95% CI, 7.7%–9.7%; p < .001) as individuals aged beyond 15 quit‐years in the PLCO, with similar results in NHIS and NLST. For example, mean 5‐year lung cancer risk for those aged 65 years with 15 quit‐years = 1.47% (95% CI, 1.35%–1.59%) versus 1.76% (95% CI, 1.62%–1.90%) for those aged 70 years with 20 quit‐years in the PLCO. Removing the quit‐year criterion would make 4.9 million more people eligible and increase the proportion of preventable lung cancer deaths prevented (sensitivity) from 63.7% to 74.2%. Alternatively, augmentation using LYFS‐CT would make 1.7 million more people eligible while increasing the lung cancer death sensitivity to 74.0%.
Conclusions
Because of aging, absolute lung cancer risk increases beyond 15 quit‐years, which does not support exemption from screening or curtailing screening once it has been initiated. Compared with relaxing the USPSTF quit‐year criterion, augmentation using LYFS‐CT could prevent most of the deaths at substantially superior efficiency, while also preventing deaths among individuals who currently smoke with low intensity or long duration.
In two cohorts, lung cancer risk increased with time since quitting smoking because of concomitant aging, calling into question the US Preventive Services Task Force (USPSTF) lung cancer screening guidelines which restrict screening to individuals with ≤15 quit‐years. Augmenting USPSTF eligibility criteria with individuals estimated to gain the most days of life based on the Life Years From Screening‐CT model of benefit from attending lung cancer screening could prevent lung cancer deaths more efficiently and fairly than relaxing the quit‐year criteria.
Primary human papillomavirus (HPV) testing (without concurrent Pap tests) every 3 years is under consideration in the United States as an alternative to the two recommended cervical cancer screening ...strategies: primary Pap testing every 3 years, or concurrent Pap and HPV testing ("cotesting") every 5 years. Using logistic regression and Weibull survival models, we estimated and compared risks of cancer and cervical intraepithelial neoplasia grade 3 or worse (CIN3+) for the three strategies among 1011092 women aged 30 to 64 years testing HPV-negative and/or Pap-negative in routine screening at Kaiser Permanente Northern California since 2003. All statistical tests were two sided. Three-year risks following an HPV-negative result were lower than 3-year risks following a Pap-negative result (CIN3+ = 0.069% vs 0.19%, P < .0001; Cancer = 0.011% vs 0.020%, P < .0001) and 5-year risks following an HPV-negative/Pap-negative cotest (CIN3+ = 0.069% vs 0.11%, P < .0001; Cancer = 0.011% vs 0.014%, P = .21). These findings suggest that primary HPV testing merits consideration as another alternative for cervical screening.
Background
Cancer incidence is higher in men than in women at most shared anatomic sites for currently unknown reasons. The authors quantified the extent to which behaviors (smoking and alcohol use), ...anthropometrics (body mass index and height), lifestyles (physical activity, diet, medications), and medical history collectively explain the male predominance of risk at 21 shared cancer sites.
Methods
Prospective cohort analyses (n = 171,274 male and n = 122,826 female participants; age range, 50–71 years) in the National Institutes of Health‐AARP Diet and Health Study (1995–2011). Cancer‐specific Cox regression models were used to estimate male‐to‐female hazard ratios (HRs). The degree to which risk factors explained the observed male–female risk disparity was quantified using the Peters–Belson method.
Results
There were 26,693 incident cancers (17,951 in men and 8742 in women). Incidence was significantly lower in men than in women only for thyroid and gallbladder cancers. At most other anatomic sites, the risks were higher in men than in women (adjusted HR range, 1.3–10.8), with the strongest increases for bladder cancer (HR, 3.33; 95% confidence interval CI, 2.93–3.79), gastric cardia cancer (HR, 3.49; 95% CI, 2.26–5.37), larynx cancer (HR, 3.53; 95% CI, 2.46–5.06), and esophageal adenocarcinoma (HR, 10.80; 95% CI, 7.33–15.90). Risk factors explained a statistically significant (nonzero) proportion of the observed male excess for esophageal adenocarcinoma and cancers of liver, other biliary tract, bladder, skin, colon, rectum, and lung. However, only a modest proportion of the male excess was explained by risk factors (ranging from 50% for lung cancer to 11% for esophageal adenocarcinoma).
Conclusions
Men have a higher risk of cancer than women at most shared anatomic sites. Such male predominance is largely unexplained by risk factors, underscoring a role for sex‐related biologic factors.
The male predominance of many nonsex‐specific cancers has been explained by differences in exposure prevalence between sexes, but cancer incidence in this study remained significantly higher among men for most sites after a comprehensive adjustment for carcinogenic exposures. These findings suggest a role of sex‐related biologic mechanisms as the major determinants of sex differences in cancer risk.
Lengthening the annual low-dose computed tomography (CT) screening interval for individuals at lowest risk of lung cancer could reduce harms and improve efficiency. We analyzed 23 328 participants in ...the National Lung Screening Trial who had a negative CT screen (no ≥4-mm nodules) to develop an individualized model for lung cancer risk after a negative CT. The Lung Cancer Risk Assessment Tool + CT (LCRAT+CT) updates "prescreening risk" (calculated using traditional risk factors) with selected CT features. At the next annual screen following a negative CT, risk of cancer detection was reduced among the 70% of participants with neither CT-detected emphysema nor consolidation (median risk = 0.2%, interquartile range IQR = 0.1%-0.3%). However, risk increased for the 30% with CT emphysema (median risk = 0.5%, IQR = 0.3%-0.8%) and the 0.6% with consolidation (median = 1.6%, IQR = 1.0%-2.5%). As one example, a threshold of next-screen risk lower than 0.3% would lengthen the interval for 57.8% of screen-negatives, thus averting 49.8% of next-screen false-positives among screen-negatives but delaying diagnosis for 23.9% of cancers. Our results support that many, but not all, screen-negatives might reasonably lengthen their CT screening interval.
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