Search

Improvements in cancer prevention and control are poised to be main contributors in reducing the burden of cancer in the US. We quantify top opportunities to accelerate progress using projected life years gained (LYG) and deaths averted as measures. We project that over the next 25 years, realistic gains from tobacco control can contribute 0.4 to 17 million additional LYG per intervention and 8.4 million additional LYG from improving uptake of screening programs over the lifetime of 25 annual cohorts. Additional opportunities include addressing modifiable risk factors (excess weight, alcohol consumption), improving methods to prevent or treat oncogenic infections, and reducing cancer health disparities. Investment is needed in the pipeline of new preventive agents and technologies for early detection to continue progress. There is also a need for additional research to improve the access to and uptake of existing and emerging interventions for cancer prevention and control and to address health disparities. These gains are undeniably within our power to realize for the US population., (Published by Oxford University Press 2024.)

Background: The COVID-19 pandemic had a significant impact on cancer screening and treatment, particularly in 2020. However, no single study has comprehensively analyzed its effects on cancer incidence and disparities among groups such as race/ethnicity, socioeconomic status (SES), persistent poverty (PP), and rurality., Methods: Utilizing the recent data from the United States National Cancer Institute's Surveillance, Epidemiology, and End Results Program, we calculated delay- and age-adjusted incidence rates for 13 cancer sites in 2020 and 2015-2019. Percent changes (PCs) of rates in 2020 compared to 2015-2019 were measured and compared across race/ethnic, census tract-level SES, PP, and rurality groups., Results: Overall, incidence rates decreased from 2015-2019 to 2020, with varying PCs by cancer sites and population groups. Notably, NH Blacks showed significantly larger PCs than NH Whites in female lung, prostate, and colon cancers (e.g., prostate cancer: NH Blacks -7.3, 95% CI: [-9.0, -5.5]; NH Whites: -3.1, 95% CI: [-3.9, -2.2]). Significantly larger PCs were observed for the lowest versus highest SES groups (prostate cancer), PP versus non-PP groups (prostate and female breast cancer), and all urban versus rural areas (prostate, female breast, female and male lung, colon, cervix, melanoma, liver, bladder, and kidney cancer)., Conclusions: The COVID-19 pandemic coincided with reduction in incidence rates in the U.S. in 2020 and was associated with worsening disparities among groups, including race/ethnicity, SES, rurality, and PP groups, across most cancer sites. Further investigation is needed to understand the specific effects of COVID-19 on different population groups of interest., (© 2024 Information Management Services, Inc. Cancer Medicine published by John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Background Monoclonal gammopathy of undetermined significance (MGUS) is the premalignant condition of multiple myeloma (MM). Given a lack of population-based screening for MGUS and its asymptomatic nature, the epidemiology of MGUS remains unknown. This study estimated age- and race/ethnicity-specific MGUS incidence and preclinical duration from MGUS to MM in the United States. Methods A previously published modeling approach was used to calculate national MGUS incidence using estimates of MGUS prevalence, MM incidence, MM-specific and all-cause mortality, and population age distribution from the National Health and Nutrition Examination Survey, 1999-2004, and Surveillance, Epidemiology, and End Results, 2000-2021. The estimated MGUS prevalence was divided by MGUS incidence to obtain preclinical duration of MM. Results MGUS incidence for non-Hispanic white (NHW) populations was 52, 86, 142, and 181 and for non-Hispanic black (NHB) population was 110, 212, 392, and 570 per 100,000 person-years at ages 50, 60, 70, and 80, respectively. The average preclinical duration was 20.5 (95% confidence interval, CI: 16.5, 26.1) years for the NHW population and 14.2 (95% CI: 11.5, 17.6) years for the NHB population. The cumulative risk of developing MGUS in age 50-85 was 2.8% (95% CI: 1.7%, 4.2%) for the NHW population and 6.1% (95% CI: 3.8%, 10.0%) for the NHB population. Conclusion NHB populations had a higher MGUS incidence rate at all ages and a shorter preclinical duration of MM compared to their NHW counterparts. Impact This study provides insights into the epidemiology of MGUS and enhances our understanding of the natural history of MM.

Background: A lag time between cancer case diagnosis and incidence reporting impedes the ability to monitor the impact of recent events on cancer incidence. Currently, the data submission standard is 22 months after a diagnosis year ends, and the reporting standard is 27.5 months after a diagnosis year ends. This paper presents the National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) Program's efforts to minimize the lag and achieve "real-time" reporting, operationalized as submission within 2 months from the end of a diagnosis year., Methods: Technology for rapidly creating a consolidated tumor case (CTC) from electronic pathology (e-path) reports is described. Statistical methods are extended to adjust for biases in incidence rates due to reporting delays for the most recent diagnosis years., Results: A registry pilot study demonstrated that real-time submissions can approximate rates obtained from 22-month submissions after adjusting for reporting delays. A plan to be implemented across the SEER Program rapidly ascertains unstructured e-path reports and uses machine learning algorithms to translate the reports into the core data items that comprise a CTC for incidence reporting. Across the program, cases were submitted 2 months after the end of the calendar year. Registries with the most promising baseline values and a willingness to modify registry operations have joined a program to become certified as real-time reporting., Conclusion: Advances in electronic reporting, natural language processing, registry operations, and statistical methodology, energized by the SEER Program's mobilization and coordination of these efforts, will make real-time reporting an achievable goal., (Published by Oxford University Press 2024.)

Background: Foreign-born populations in the United States have markedly increased, yet cancer trends remain unexplored. Survey-based Population-Adjusted Rate Calculator (SPARC) is a new tool for evaluating nativity differences in cancer mortality., Methods: Using SPARC, we calculated 3-year (2016-2018) age-adjusted mortality rates and rate ratios for common cancers by sex, age group, race and ethnicity, and nativity. Trends by nativity were examined for the first time for 2006-2018. Traditional cancer statistics draw populations from decennial censuses. However, nativity-stratified populations are from the American Community Surveys, thus involve sampling errors. To rectify this, SPARC employed bias-corrected estimators. Death counts came from the National Vital Statistics System., Results: Age-adjusted mortality rates were higher among US-born populations across nearly all cancer types, with the largest US-born, foreign-born difference observed in lung cancer among Black women (rate ratio = 3.67, 95% confidence interval [CI] = 3.37 to 4.00). The well-documented White-Black differences in breast cancer mortality existed mainly among US-born women. For all cancers combined, descending trends were more accelerated for US-born compared with foreign-born individuals in all race and ethnicity groups with changes ranging from -2.6% per year in US-born Black men to stable (statistically nonsignificant) among foreign-born Black women. Pancreas and liver cancers were exceptions with increasing, stable, or decreasing trends depending on nativity and race and ethnicity. Notably, foreign-born Black men and foreign-born Hispanic men did not show a favorable decline in colorectal cancer mortality., Conclusions: Although all groups show beneficial cancer mortality trends, those with higher rates in 2006 have experienced sharper declines. Persistent disparities between US-born and foreign-born individuals, especially among Black people, necessitate further investigation., (Published by Oxford University Press 2024.)

Importance: The effects of breast cancer incidence changes and advances in screening and treatment on outcomes of different screening strategies are not well known., Objective: To estimate outcomes of various mammography screening strategies., Design, Setting, and Population: Comparison of outcomes using 6 Cancer Intervention and Surveillance Modeling Network (CISNET) models and national data on breast cancer incidence, mammography performance, treatment effects, and other-cause mortality in US women without previous cancer diagnoses., Exposures: Thirty-six screening strategies with varying start ages (40, 45, 50 years) and stop ages (74, 79 years) with digital mammography or digital breast tomosynthesis (DBT) annually, biennially, or a combination of intervals. Strategies were evaluated for all women and for Black women, assuming 100% screening adherence and "real-world" treatment., Main Outcomes and Measures: Estimated lifetime benefits (breast cancer deaths averted, percent reduction in breast cancer mortality, life-years gained), harms (false-positive recalls, benign biopsies, overdiagnosis), and number of mammograms per 1000 women., Results: Biennial screening with DBT starting at age 40, 45, or 50 years until age 74 years averted a median of 8.2, 7.5, or 6.7 breast cancer deaths per 1000 women screened, respectively, vs no screening. Biennial DBT screening at age 40 to 74 years (vs no screening) was associated with a 30.0% breast cancer mortality reduction, 1376 false-positive recalls, and 14 overdiagnosed cases per 1000 women screened. Digital mammography screening benefits were similar to those for DBT but had more false-positive recalls. Annual screening increased benefits but resulted in more false-positive recalls and overdiagnosed cases. Benefit-to-harm ratios of continuing screening until age 79 years were similar or superior to stopping at age 74. In all strategies, women with higher-than-average breast cancer risk, higher breast density, and lower comorbidity level experienced greater screening benefits than other groups. Annual screening of Black women from age 40 to 49 years with biennial screening thereafter reduced breast cancer mortality disparities while maintaining similar benefit-to-harm trade-offs as for all women., Conclusions: This modeling analysis suggests that biennial mammography screening starting at age 40 years reduces breast cancer mortality and increases life-years gained per mammogram. More intensive screening for women with greater risk of breast cancer diagnosis or death can maintain similar benefit-to-harm trade-offs and reduce mortality disparities.

Importance: Standard cancer prognosis models typically do not include much specificity in characterizing competing illnesses or general health status when providing prognosis estimates, limiting their utility for individuals, who must consider their cancer in the context of their overall health. This is especially true for patients with oral cancer, who frequently have competing illnesses., Objective: To describe a statistical framework and accompanying new publicly available calculator that provides personalized estimates of the probability of a patient surviving or dying from cancer or other causes, using oral cancer as the first data set., Design, Setting, and Participants: The models used data from the Surveillance, Epidemiology, and End Results (SEER) 18 registry (2000 to 2011), SEER-Medicare linked files, and the National Health Interview Survey (NHIS) (1986 to 2009). Statistical methods developed to calculate natural life expectancy in the absence of the cancer, cancer-specific survival, and other-cause survival were applied to oral cancer data and internally validated with 10-fold cross-validation. Eligible participants were aged between 20 and 94 years with oral squamous cell carcinoma., Exposures: Histologically confirmed oral cancer, general health status, smoking, and selected serious comorbid conditions., Main Outcomes and Measures: Probabilities of surviving or dying from the cancer or from other causes, and life expectancy in the absence of the cancer., Results: A total of 22 392 patients with oral squamous cell carcinoma (13 544 male [60.5%]; 1476 Asian and Pacific Islander [6.7%]; 1792 Black [8.0%], 1589 Hispanic [7.2%], 17 300 White [78.1%]) and 402 626 NHIS interviewees were included in this calculator designed for public use for patients ages 20 to 86 years with newly diagnosed oral cancer to obtain estimates of health status-adjusted age, life expectancy in the absence of the cancer, and the probability of surviving, dying from the cancer, or dying from other causes within 1 to 10 years after diagnosis. The models in the calculator estimated that patients with oral cancer have a higher risk of death from other causes than their matched US population, and that this risk increases by stage., Conclusions and Relevance: The models developed for the calculator demonstrate that survival estimates that exclude the effects of coexisting conditions can lead to underestimates or overestimates of survival. This new calculator approach will be broadly applicable for developing future prognostic models of cancer and noncancer aspects of a person's health in other cancers; as registries develop more linkages, available covariates will become broader, strengthening future tools.

Importance: Increased use of recommended screening could help achieve the Cancer Moonshot goal of reducing US cancer deaths., Objective: To estimate the number of cancer deaths that could be prevented with a 10-percentage point increase in the use of US Preventive Services Task Force (USPSTF)-recommended screening., Design, Setting, and Participants: This decision analytical model study is an extension of previous studies conducted for the USPSTF from 2018 to 2023. This study simulated contemporary cohorts of US adults eligible for lung, colorectal, breast, and cervical cancer screening., Exposures: Annual low-dose computed lung tomography among eligible adults aged 50 to 80 years; colonoscopy every 10 years among adults aged 45 to 75 years; biennial mammography among female adults aged 40 to 74 years; and triennial cervical cytology screening among female adults aged 21 to 29 years, followed by human papillomavirus testing every 5 years from ages 30 to 65 years., Main Outcomes and Measures: Estimated number of cancer deaths prevented with a 10-percentage point increase in screening use, assuming screening commences at the USPSTF-recommended starting age and continues throughout the lifetime. Outcomes were presented 2 ways: (1) per 100 000 and (2) among US adults in 2021; and they were expressed among the target population at the age of screening initiation. For lung cancer, estimates were among those who will also meet the smoking eligibility criteria during their lifetime. Harms from increased uptake were also reported., Results: A 10-percentage point increase in screening use at the age that USPSTF recommended screening commences was estimated to prevent 226 lung cancer deaths (range across models within the cancer site, 133-332 deaths), 283 (range, 263-313) colorectal cancer deaths, 82 (range, 61-106) breast cancer deaths, and 81 (1 model; no range available) cervical cancer deaths over the lifetimes of 100 000 persons eligible for screening. These rates corresponded with an estimated 1010 (range, 590-1480) lung cancer deaths prevented, 11 070 (range, 10 280-12 250) colorectal cancer deaths prevented, 1790 (range, 1330-2310) breast cancer deaths prevented, and 1710 (no range available) cervical cancer deaths prevented over the lifetimes of eligible US residents at the recommended age to initiate screening in 2021. Increased uptake was also estimated to generate harms, including 100 000 (range, 45 000-159 000) false-positive lung scans, 6000 (range, 6000-7000) colonoscopy complications, 300 000 (range, 295 000-302 000) false-positive mammograms, and 348 000 (no range available) colposcopies over the lifetime., Conclusions and Relevance: In this decision analytical model study, a 10-percentage point increase in uptake of USPSTF-recommended lung, colorectal, breast, and cervical cancer screening at the recommended starting age was estimated to yield important reductions in cancer deaths. Achieving these reductions is predicated on ensuring equitable access to screening.

Importance: In the setting of a new cancer diagnosis, the focus is usually on the cancer as the main threat to survival, but people may have other conditions that pose an equal or greater threat to their life than their cancer: a competing risk of death. This is especially true for patients who have cancer of the oral cavity, because prolonged exposure to alcohol and tobacco are risk factors for cancer in this location but also can result in medical conditions with the potential to shorten life expectancy, competing as a cause of death that may intervene in conjunction with or before the cancer., Observations: A calculator designed for public use has been released that allows patients age 20 to 86 years who have a newly diagnosed oral cancer to obtain estimates of their health status-adjusted age, life expectancy in the absence of the cancer, and probability of surviving, dying of the cancer, or dying of other causes within 1 to 10 years after diagnosis. The models in the calculator showed that patients with oral cavity cancer had a higher than average risk of death from other causes than the matched US population, and this risk increases by stage., Conclusions and Relevance: The Surveillance, Epidemiology and End Results Program Oral Cancer Survival Calculator supports a holistic approach to the life of the patient, and the risk of death of other causes is treated equally to consideration of the probability of death of the cancer. This tool may be usefully paired with the other available prognostic calculators for oral cancer and is an example of the possibilities now available with registry linkages to partially overlapping or independent data sets and statistical techniques that allow the use of 2 time scales in 1 analysis.

The considerable deficit in cancer diagnoses in 2020 due to COVID-19 pandemic disruptions in health care can pose challenges in the estimation and interpretation of long-term cancer trends. Using Surveillance, Epidemiology, and End Results (SEER) (2000-2020) data, we demonstrate that inclusion of the 2020 incidence rates in joinpoint models to estimate trends can result in a poorer fit to the data and less accurate or less precise trend estimates, providing challenges in the interpretation of the estimates as a cancer control measure. To measure the decline in 2020 relative to 2019 cancer incidence rates, we used the percent change of rates in 2020 compared with 2019. Overall, SEER cancer incidence rates dropped approximately 10% in 2020, but for thyroid cancer the decrease was as large as 18% after adjusting for reporting delay. The 2020 SEER incidence data are available in all SEER released products, except for joinpoint estimates of trends and lifetime risk of developing cancer., (Published by Oxford University Press 2023.)

Importance: To make wise decisions about the health risks they face, people need information about the magnitude of the threats as well as the context, such as how risks compare. Such information is often presented by age, sex, and race but rarely accounts for smoking status, a major risk factor for many causes of death., Objective: To update the National Cancer Institute's Know Your Chances website to present mortality estimates for a broad set of causes of death and all causes combined by smoking status in addition to age, sex, and race., Design, Setting, and Participants: In this cohort study, mortality estimates using life table methods were calculated with the National Cancer Institute's DevCan software package, combining data from the US National Vital Statistics System, National Health Interview Survey-Linked Mortality Files, National Institutes of Health-AARP (American Association of Retired Persons), Cancer Prevention Study II, Nurses' Health and Health Professions follow-up studies, and Women's Health Initiative. Data were collected from January 1, 2009, to December 31, 2018, and analyzed from August 27, 2019, to February 28, 2023., Main Outcomes and Measures: Age-conditional probabilities of dying due to various causes and all causes combined, accounting for competing causes of death, for people aged 20 to 75 years over the next 5, 10, or 20 years by sex, race, and smoking status., Results: A total of 954 029 individuals aged 55 years or older (55.8% women) were included in the analysis. Regardless of sex or race, for never-smokers, coronary heart disease represented the highest 10-year chance of death after about 50 years of age, which is higher than for any malignant neoplasm. Among current smokers, the 10-year chance of death due to lung cancer was almost as high as for coronary heart disease in each group. For Black and White female current smokers aged from the mid-40s onward, the 10-year probability of death due to lung cancer was substantially higher than for breast cancer. After 40 years of age, the observed effect of never vs current smoking on the 10-year chance of death due to all causes approximated adding 10 years of age. After 40 years of age when conditioning on smoking status, mortality risk for Black individuals was approximately that of White individuals 5 years older., Conclusions and Relevance: Using life table methods and accounting for competing risks, the revised Know Your Chances website presents age-conditional mortality estimates according to smoking status for a broad set of causes in the context of other conditions and all-cause mortality. The findings of this cohort study suggest that failing to account for smoking status results in inaccurate mortality estimates for many causes-namely, they are too low for smokers and too high for nonsmokers.

Background: In their 2021 lung cancer screening recommendation update, the U.S. Preventive Services Task Force (USPSTF) evaluated strategies that select people based on their personal lung cancer risk (risk model-based strategies), highlighting the need for further research on the benefits and harms of risk model-based screening., Objective: To evaluate and compare the cost-effectiveness of risk model-based lung cancer screening strategies versus the USPSTF recommendation and to explore optimal risk thresholds., Design: Comparative modeling analysis., Data Sources: National Lung Screening Trial; Surveillance, Epidemiology, and End Results program; U.S. Smoking History Generator., Target Population: 1960 U.S. birth cohort., Time Horizon: 45 years., Perspective: U.S. health care sector., Intervention: Annual low-dose computed tomography in risk model-based strategies that start screening at age 50 or 55 years, stop screening at age 80 years, with 6-year risk thresholds between 0.5% and 2.2% using the PLCOm2012 model., Outcome Measures: Incremental cost-effectiveness ratio (ICER) and cost-effectiveness efficiency frontier connecting strategies with the highest health benefit at a given cost., Results of Base-Case Analysis: Risk model-based screening strategies were more cost-effective than the USPSTF recommendation and exclusively comprised the cost-effectiveness efficiency frontier. Among the strategies on the efficiency frontier, those with a 6-year risk threshold of 1.2% or greater were cost-effective with an ICER less than $100 000 per quality-adjusted life-year (QALY). Specifically, the strategy with a 1.2% risk threshold had an ICER of $94 659 (model range, $72 639 to $156 774), yielding more QALYs for less cost than the USPSTF recommendation, while having a similar level of screening coverage (person ever-screened 21.7% vs. USPSTF's 22.6%)., Results of Sensitivity Analyses: Risk model-based strategies were robustly more cost-effective than the 2021 USPSTF recommendation under varying modeling assumptions., Limitation: Risk models were restricted to age, sex, and smoking-related risk predictors., Conclusion: Risk model-based screening is more cost-effective than the USPSTF recommendation, thus warranting further consideration., Primary Funding Source: National Cancer Institute (NCI).

The reporting and analysis of population-based cancer statistics in the United States has traditionally been done for counties. However, counties are not ideal for analysis of cancer rates, due to wide variation in population size, with larger counties having considerable sociodemographic variation within their borders and sparsely populated counties having less reliable estimates of cancer rates that are often suppressed due to confidentiality concerns. There is a need and an opportunity to utilize zone design procedures in the context of cancer surveillance to generate coherent, statistically stable geographic units that are more optimal for cancer reporting and analysis than counties. To achieve this goal, we sought to create areas within each US state that are: 1) similar in population size and large enough to minimize rate suppression; 2) sociodemographically homogeneous; 3) compact; and 4) custom crafted to represent areas that are meaningful to cancer registries and stakeholders. The resulting geographic units reveal the heterogeneity of rates that are hidden when reported at the county-level while substantially reducing the need to suppress data. We believe this effort will facilitate more meaningful comparative analysis of cancer rates for small geographic areas and will advance the understanding of cancer burden in the United States., (Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2022.)

Since its release of Version 1.0 in 1998, Joinpoint software developed for cancer trend analysis by a team at the US National Cancer Institute has received a considerable attention in the trend analysis community and it became one of most widely used software for trend analysis. The paper published in Statistics in Medicine in 2000 (a previous study) describes the permutation test procedure to select the number of joinpoints, and Joinpoint Version 1.0 implemented the permutation procedure as the default model selection method and employed parametric methods for the asymptotic inference of the model parameters. Since then, various updates and extensions have been made in Joinpoint software. In this paper, we review basic features of Joinpoint, summarize important updates of Joinpoint software since its first release in 1998, and provide more information on two major enhancements. More specifically, these enhancements overcome prior limitations in both the accuracy and computational efficiency of previously used methods. The enhancements include: (i) data driven model selection methods which are generally more accurate under a broad range of data settings and more computationally efficient than the permutation test and (ii) the use of the empirical quantile method for construction of confidence intervals for the slope parameters and the location of the joinpoints, which generally provides more accurate coverage than the prior parametric methods used. We show the impact of these changes in cancer trend analysis published by the US National Cancer Institute., (© 2022 John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

With increased attention to the financing and structure of healthcare, dramatic increases in the cost of diagnosing and treating cancer, and corresponding disparities in access, the study of healthcare economics and delivery has become increasingly important. The Healthcare Delivery Research Program (HDRP) in the Division of Cancer Control and Population Sciences at the National Cancer Institute (NCI) was formed in 2015 to provide a hub for cancer-related healthcare delivery and economics research. However, the roots of this program trace back much farther, at least to the formation of the NCI Division of Cancer Prevention and Control in 1983. The creation of a division focused on understanding and explaining trends in cancer morbidity and mortality was instrumental in setting the direction of cancer-related healthcare delivery and health economics research over the subsequent decades. In this commentary, we provide a brief history of health economics and healthcare delivery research at NCI, describing the organizational structure and highlighting key initiatives developed by the division, and also briefly discuss future directions. HDRP and its predecessors have supported the growth and evolution of these fields through the funding of grants and contracts; the development of data, tools, and other research resources; and thought leadership including stimulation of research on previously understudied topics. As the availability of new data, methods, and computing capacity to evaluate cancer-related healthcare delivery and economics expand, HDRP aims to continue to support this growth and evolution., (Published by Oxford University Press 2022. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Selecting the number of change points in segmented line regression is an important problem in trend analysis, and there have been various approaches proposed in the literature. We first study the empirical properties of several model selection procedures and propose a new method based on two Schwarz type criteria, a classical Bayes Information Criterion (BIC) and the one with a harsher penalty than BIC ( BIC 3 ). The proposed rule is designed to use the former when effect sizes are small and the latter when the effect sizes are large and employs the partial R 2 to determine the weight between BIC and BIC 3 . The proposed method is computationally much more efficient than the permutation test procedure that has been the default method of Joinpoint software developed for cancer trend analysis, and its satisfactory performance is observed in our simulation study. Simulations indicate that the proposed method performs well in keeping the probability of correct selection at least as large as that of BIC 3 , whose performance is comparable to that of the permutation test procedure, and improves BIC 3 when it performs worse than BIC . The proposed method is applied to the U.S. prostate cancer incidence and mortality rates., Competing Interests: No potential conflict of interest was reported by the authors., (© 2022 Informa UK Limited, trading as Taylor & Francis Group.)

Background: Life expectancy is increasingly incorporated in evidence-based screening and treatment guidelines to facilitate patient-centered clinical decision-making. However, life expectancy estimates from standard life tables do not account for health status, an important prognostic factor for premature death. This study aims to address this research gap and develop life tables incorporating the health status of adults in the United States., Methods: Data from the National Health Interview Survey (1986-2004) linked to mortality follow-up through to 2006 (age ≥ 40, n = 729,531) were used to develop life tables. The impact of self-rated health (excellent, very good, good, fair, poor) on survival was quantified in 5-year age groups, incorporating complex survey design and weights. Life expectancies were estimated by extrapolating the modeled survival probabilities., Results: Life expectancies incorporating health status differed substantially from standard US life tables and by health status. Poor self-rated health more significantly affected the survival of younger compared to older individuals, resulting in substantial decreases in life expectancy. At age 40 years, hazards of dying for white men who reported poor vs. excellent health was 8.5 (95% CI: 7.0,10.3) times greater, resulting in a 23-year difference in life expectancy (poor vs. excellent: 22 vs. 45), while at age 80 years, the hazards ratio was 2.4 (95% CI: 2.1, 2.8) and life expectancy difference was 5 years (5 vs. 10). Relative to the US general population, life expectancies of adults (age < 65) with poor health were approximately 5-15 years shorter., Conclusions: Considerable shortage in life expectancy due to poor self-rated health existed. The life table developed can be helpful by including a patient perspective on their health and be used in conjunction with other predictive models in clinical decision making, particularly for younger adults in poor health, for whom life tables including comorbid conditions are limited., (© 2022. The Author(s).)

Introduction: In 2021, the U.S. Preventive Services Task Force (USPSTF) revised its lung cancer screening recommendations expanding its eligibility. As more smokers become eligible, cessation interventions at the point of screening could enhance the benefits. Here, we evaluate the effects of joint screening and cessation interventions under the new recommendations., Methods: A validated lung cancer natural history model was used to estimate lifetime number of low-dose computed tomography screens, percentage ever screened, lung cancer deaths, lung cancer deaths averted, and life-years gained for the 1960 U.S. birth cohort aged 45 to 90 years (4.5 million individuals). Screening occurred according to the USPSTF 2013 and 2021 recommendations with varying uptake (0%, 30%, 100%), with or without a cessation intervention at the point of screening with varying effectiveness (15%, 100%)., Results: Screening 30% of the eligible population according to the 2021 criteria with no cessation intervention (USPSTF 2021, 30% uptake, without cessation intervention) was estimated to result in 6845 lung cancer deaths averted and 103,725 life-years gained. These represent 28% and 34% increases, respectively, relative to screening according to the 2013 guidelines (USPSTF 2013, 30% uptake, without cessation intervention). Adding a cessation intervention at the time of the first screen with 15% effectiveness (USPSTF 2021, 30% uptake, with cessation intervention with 15% effectiveness) was estimated to result in 2422 additional lung cancer deaths averted (9267 total, ∼73% increase versus USPSTF 2013, 30% uptake, without cessation intervention) and 322,785 life-years gained (∼318% increase). Screening 100% of the eligible according to the 2021 guidelines with no cessation intervention (USPSTF 2021, 100% uptake, without cessation intervention) was estimated to result in 23,444 lung cancer deaths averted (∼337% increase versus USPSTF 2013, 30% uptake, without cessation intervention) and 354,330 life-years gained (∼359% increase). Adding a cessation intervention with 15% effectiveness (USPSTF 2021, 100% uptake, with cessation intervention with 15% effectiveness) would result in 31,998 lung cancer deaths averted (∼497% increase versus USPSTF 2013, 30% uptake, without cessation intervention) and 1,086,840 life-years gained (∼1309% increase)., Conclusions: Joint screening and cessation interventions would result in considerable lung cancer deaths averted and life-years gained. Adding a one-time cessation intervention of modest effectiveness (15%) results in comparable life-years gained as increasing screening uptake from 30% to 100% because while cessation decreases mortality from many causes, screening only reduces lung cancer mortality. This simulation indicates that incorporating cessation programs into screening practice should be a priority as it can maximize overall benefits., (Copyright © 2021 International Association for the Study of Lung Cancer. All rights reserved.)

Importance: The US Preventive Services Task Force (USPSTF) issued its 2021 recommendation on lung cancer screening, which lowered the starting age for screening from 55 to 50 years and the minimum cumulative smoking exposure from 30 to 20 pack-years relative to its 2013 recommendation. Although costs are expected to increase because of the expanded screening eligibility criteria, it is unknown whether the new guidelines for lung cancer screening are cost-effective., Objective: To evaluate the cost-effectiveness of the 2021 USPSTF recommendation for lung cancer screening compared with the 2013 recommendation and to explore the cost-effectiveness of 6 alternative screening strategies that maintained a minimum cumulative smoking exposure of 20 pack-years and an ending age for screening of 80 years but varied the starting ages for screening (50 or 55 years) and the number of years since smoking cessation (≤15, ≤20, or ≤25)., Design, Setting, and Participants: A comparative cost-effectiveness analysis using 4 independently developed microsimulation models that shared common inputs to assess the population-level health benefits and costs of the 2021 recommended screening strategy and 6 alternative screening strategies compared with the 2013 recommended screening strategy. The models simulated a 1960 US birth cohort. Simulated individuals entered the study at age 45 years and were followed up until death or age 90 years, corresponding to a study period from January 1, 2005, to December 31, 2050., Exposures: Low-dose computed tomography in lung cancer screening programs with a minimum cumulative smoking exposure of 20 pack-years., Main Outcomes and Measures: Incremental cost-effectiveness ratio (ICER) per quality-adjusted life-year (QALY) of the 2021 vs 2013 USPSTF lung cancer screening recommendations as well as 6 alternative screening strategies vs the 2013 USPSTF screening strategy. Strategies with a mean ICER lower than $100 000 per QALY were deemed cost-effective., Results: The 2021 USPSTF recommendation was estimated to be cost-effective compared with the 2013 recommendation, with a mean ICER of $72 564 (range across 4 models, $59 493-$85 837) per QALY gained. The 2021 recommendation was not cost-effective compared with 6 alternative strategies that used the 20 pack-year criterion. Strategies associated with the most cost-effectiveness included those that expanded screening eligibility to include a greater number of former smokers who had not smoked for a longer duration (ie, ≤20 years and ≤25 years since smoking cessation vs ≤15 years since smoking cessation). In particular, the strategy that screened former smokers who quit within the past 25 years and began screening at age 55 years was associated with screening coverage closest to that of the 2021 USPSTF recommendation yet yielded greater cost-effectiveness, with a mean ICER of $66 533 (range across 4 models, $55 693-$80 539)., Conclusions and Relevance: This economic evaluation found that the 2021 USPSTF recommendation for lung cancer screening was cost-effective; however, alternative screening strategies that maintained a minimum cumulative smoking exposure of 20 pack-years but included individuals who quit smoking within the past 25 years may be more cost-effective and warrant further evaluation.

Background: The American Cancer Society (ACS) and the NCI collaborate every 5 to 8 years to update the methods for estimating the numbers of new cancer cases and deaths in the current year for the U.S. and individual states. Herein, we compare our current projection methodology with the next generation of statistical models., Methods: A validation study was conducted comparing current projection methods (vector autoregression for incidence; Joinpoint regression for mortality) with the Bayes state-space method and novel Joinpoint algorithms. Incidence data from 1996-2010 were projected to 2014 using two inputs: modeled data and observed data with modeled where observed were missing. For mortality, observed data from 1995 to 2009, 1996 to 2010, 1997 to 2011, and 1998 to 2012, each projected 3 years forward to 2012 to 2015. Projection methods were evaluated using the average absolute relative deviation (AARD) between observed counts (2014 for incidence, 2012-2015 for mortality) and estimates for 47 cancer sites nationally and 21 sites by state., Results: A novel Joinpoint model provided a good fit for both incidence and mortality, particularly for the most common cancers in the U.S. Notably, the AARD for cancers with cases in 2014 exceeding 49,000 for this model was 3.4%, nearly half that of the current method (6.3%)., Conclusions: A data-driven Joinpoint algorithm had versatile performance at the national and state levels and will replace the ACS's current methods., Impact: This methodology provides estimates of cancer data that are not available for the current year, thus continuing to fill an important gap for advocacy, research, and public health planning., (©2021 American Association for Cancer Research.)

Background: Improvements in cancer survival are usually assessed by comparing survival in grouped years of diagnosis. To enhance analyses of survival trends, we present the joinpoint survival model webtool (JPSurv) that analyzes survival data by single year of diagnosis and estimates changes in survival trends and year-over-year trend measures., Methods: We apply JPSurv to relative survival data for individuals diagnosed with female breast cancer, melanoma cancer, non-Hodgkin lymphoma (NHL), and chronic myeloid leukemia (CML) between 1975 and 2015 in the Surveillance, Epidemiology, and End Results Program. We estimate the number and location of joinpoints and the trend measures and provide interpretation., Results: In general, relative survival has substantially improved at least since the mid-1990s for all cancer sites. The largest improvements in 5-year relative survival were observed for distant-stage melanoma after 2009, which increased by almost 3 survival percentage points for each subsequent year of diagnosis, followed by CML in 1995-2010, and NHL in 1995-2003. The modeling also showed that for patients diagnosed with CML after 1995 (compared with before), there was a greater decrease in the probability of dying of the disease in the 4th and 5th years after diagnosis compared with the initial years since diagnosis., Conclusions: The greatest increases in trends for distant melanoma, NHL, and CML coincided with the introduction of novel treatments, demonstrating the value of JPSurv for estimating and interpreting cancer survival trends., Impact: The JPSurv webtool provides a suite of estimates for analyzing trends in cancer survival that complement traditional descriptive survival analyses., (©2021 The Authors; Published by the American Association for Cancer Research.)

Background: The American Cancer Society (ACS) and the NCI collaborate every 5-8 years to update the methods for estimating numbers of new cancer cases and deaths in the current year in the United States and in every state and the District of Columbia. In this article, we reevaluate the statistical method for estimating unavailable historical incident cases which are needed for projecting the current year counts., Methods: We compared the current county-level model developed in 2012 (M0) with three new models, including a state-level mixed effect model (M1) and two state-level hierarchical Bayes models with varying random effects (M2 and M3). We used 1996-2014 incidence data for 16 sex-specific cancer sites to fit the models. An average absolute relative deviation (AARD) comparing the observed with the model-specific predicted counts was calculated for each site. Models were also cross-validated for six selected sex-specific cancer sites., Results: For the cross-validation, the AARD ranged from 2.8% to 33.0% for M0, 3.3% to 31.1% for M1, 6.6% to 30.5% for M2, and 10.4% to 393.2% for M3. M1 encountered the least technical issues in terms of model convergence and running time., Conclusions: The state-level mixed effect model (M1) was overall superior in accuracy and computational efficiency and will be the new model for the ACS current year projection project., Impact: In addition to predicting the unavailable state-level historical incidence counts for cancer surveillance, the updated algorithms have broad applicability for disease mapping and other activities of public health planning, advocacy, and research., (©2021 American Association for Cancer Research.)

Importance: The US Preventive Services Task Force (USPSTF) is updating its 2016 colorectal cancer screening recommendations., Objective: To provide updated model-based estimates of the benefits, burden, and harms of colorectal cancer screening strategies and to identify strategies that may provide an efficient balance of life-years gained (LYG) from screening and colonoscopy burden to inform the USPSTF., Design, Setting, and Participants: Comparative modeling study using 3 microsimulation models of colorectal cancer screening in a hypothetical cohort of 40-year-old US individuals at average risk of colorectal cancer., Exposures: Screening from ages 45, 50, or 55 years to ages 70, 75, 80, or 85 years with fecal immunochemical testing (FIT), multitarget stool DNA testing, flexible sigmoidoscopy alone or with FIT, computed tomography colonography, or colonoscopy. All persons with an abnormal noncolonoscopy screening test result were assumed to undergo follow-up colonoscopy. Screening intervals varied by test. Full adherence with all procedures was assumed., Main Outcome and Measures: Estimated LYG relative to no screening (benefit), lifetime number of colonoscopies (burden), number of complications from screening (harms), and balance of incremental burden and benefit (efficiency ratios). Efficient strategies were those estimated to require fewer additional colonoscopies per additional LYG relative to other strategies., Results: Estimated LYG from screening strategies ranged from 171 to 381 per 1000 40-year-olds. Lifetime colonoscopy burden ranged from 624 to 6817 per 1000 individuals, and screening complications ranged from 5 to 22 per 1000 individuals. Among the 49 strategies that were efficient options with all 3 models, 41 specified screening beginning at age 45. No single age to end screening was predominant among the efficient strategies, although the additional LYG from continuing screening after age 75 were generally small. With the exception of a 5-year interval for computed tomography colonography, no screening interval predominated among the efficient strategies for each modality. Among the strategies highlighted in the 2016 USPSTF recommendation, lowering the age to begin screening from 50 to 45 years was estimated to result in 22 to 27 additional LYG, 161 to 784 additional colonoscopies, and 0.1 to 2 additional complications per 1000 persons (ranges are across screening strategies, based on mean estimates across models). Assuming full adherence, screening outcomes and efficient strategies were similar by sex and race and across 3 scenarios for population risk of colorectal cancer., Conclusions and Relevance: This microsimulation modeling analysis suggests that screening for colorectal cancer with stool tests, endoscopic tests, or computed tomography colonography starting at age 45 years provides an efficient balance of colonoscopy burden and life-years gained.

Importance: The US Preventive Services Task Force (USPSTF) is updating its 2013 lung cancer screening guidelines, which recommend annual screening for adults aged 55 through 80 years who have a smoking history of at least 30 pack-years and currently smoke or have quit within the past 15 years., Objective: To inform the USPSTF guidelines by estimating the benefits and harms associated with various low-dose computed tomography (LDCT) screening strategies., Design, Setting, and Participants: Comparative simulation modeling with 4 lung cancer natural history models for individuals from the 1950 and 1960 US birth cohorts who were followed up from aged 45 through 90 years., Exposures: Screening with varying starting ages, stopping ages, and screening frequency. Eligibility criteria based on age, cumulative pack-years, and years since quitting smoking (risk factor-based) or on age and individual lung cancer risk estimation using risk prediction models with varying eligibility thresholds (risk model-based). A total of 1092 LDCT screening strategies were modeled. Full uptake and adherence were assumed for all scenarios., Main Outcomes and Measures: Estimated lung cancer deaths averted and life-years gained (benefits) compared with no screening. Estimated lifetime number of LDCT screenings, false-positive results, biopsies, overdiagnosed cases, and radiation-related lung cancer deaths (harms)., Results: Efficient screening programs estimated to yield the most benefits for a given number of screenings were identified. Most of the efficient risk factor-based strategies started screening at aged 50 or 55 years and stopped at aged 80 years. The 2013 USPSTF-recommended criteria were not among the efficient strategies for the 1960 US birth cohort. Annual strategies with a minimum criterion of 20 pack-years of smoking were efficient and, compared with the 2013 USPSTF-recommended criteria, were estimated to increase screening eligibility (20.6%-23.6% vs 14.1% of the population ever eligible), lung cancer deaths averted (469-558 per 100 000 vs 381 per 100 000), and life-years gained (6018-7596 per 100 000 vs 4882 per 100 000). However, these strategies were estimated to result in more false-positive test results (1.9-2.5 per person screened vs 1.9 per person screened with the USPSTF strategy), overdiagnosed lung cancer cases (83-94 per 100 000 vs 69 per 100 000), and radiation-related lung cancer deaths (29.0-42.5 per 100 000 vs 20.6 per 100 000). Risk model-based vs risk factor-based strategies were estimated to be associated with more benefits and fewer radiation-related deaths but more overdiagnosed cases., Conclusions and Relevance: Microsimulation modeling studies suggested that LDCT screening for lung cancer compared with no screening may increase lung cancer deaths averted and life-years gained when optimally targeted and implemented. Screening individuals at aged 50 or 55 years through aged 80 years with 20 pack-years or more of smoking exposure was estimated to result in more benefits than the 2013 USPSTF-recommended criteria and less disparity in screening eligibility by sex and race/ethnicity.

Cancer incidence and mortality are typically presented as age-standardized rates. Inference about these rates becomes complicated when denominators involve sampling errors. We propose a bias-corrected rate estimator as well as its corresponding variance estimator that take into account sampling errors in the denominators. Confidence intervals are derived based on the proposed estimators as well. Performance of the proposed methods is evaluated empirically based on simulation studies. More importantly, advantage of the proposed method is demonstrated and verified in a real-life study of cancer mortality disparity. A web-based, user-friendly computational tool is also being developed at the National Cancer Institute to accompany the new methods with the first application being calculating cancer mortality rates by US-born and foreign-born status. Finally, promise of proposed estimators to account for errors introduced by differential privacy procedures to the 2020 decennial census products is discussed.

Background: Current US Preventive Services Task Force (USPSTF) lung cancer screening guidelines are based on smoking history and age (55-80 years). These guidelines may miss those at higher risk, even at lower exposures of smoking or younger ages, because of other risk factors such as race, family history, or comorbidity. In this study, we characterized the demographic and clinical profiles of those selected by risk-based screening criteria but were missed by USPSTF guidelines in younger (50-54 years) and older (71-80 years) age groups., Methods: We used data from the National Health Interview Survey, the CISNET Smoking History Generator, and results of logistic prediction models to simulate lifetime lung cancer risk-factor data for 100 000 individuals in the 1950-1960 birth cohorts. We calculated age-specific 6-year lung cancer risk for each individual from ages 50 to 90 years using the PLCOm2012 model and evaluated age-specific screening eligibility by USPSTF guidelines and by risk-based criteria (varying thresholds between 1.3% and 2.5%)., Results: In the 1950 birth cohort, 5.4% would have been ineligible for screening by USPSTF criteria in their younger ages but eligible based on risk-based criteria. Similarly, 10.4% of the cohort would be ineligible for screening by USPSTF in older ages. Notably, high proportions of blacks were ineligible for screening by USPSTF criteria at younger (15.6%) and older (14.2%) ages, which were statistically significantly greater than those of whites (4.8% and 10.8%, respectively; P < .001). Similar results were observed with other risk thresholds and for the 1960 cohort., Conclusions: Further consideration is needed to incorporate comprehensive risk factors, including race and ethnicity, into lung cancer screening to reduce potential racial disparities., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Lung cancer is made up of distinct subtypes, including non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC). Although overall mortality from lung cancer has been declining in the United States, little is known about mortality trends according to cancer subtype at the population level because death certificates do not record subtype information., Methods: Using data from Surveillance, Epidemiology, and End Results (SEER) areas, we assessed lung-cancer mortality and linked deaths from lung cancer to incident cases in SEER cancer registries. This allowed us to evaluate population-level mortality trends attributed to specific subtypes (incidence-based mortality). We also evaluated lung-cancer incidence and survival according to cancer subtype, sex, and calendar year. Joinpoint software was used to assess changes in incidence and trends in incidence-based mortality., Results: Mortality from NSCLC decreased even faster than the incidence of this subtype, and this decrease was associated with a substantial improvement in survival over time that corresponded to the timing of approval of targeted therapy. Among men, incidence-based mortality from NSCLC decreased 6.3% annually from 2013 through 2016, whereas the incidence decreased 3.1% annually from 2008 through 2016. Corresponding lung cancer-specific survival improved from 26% among men with NSCLC that was diagnosed in 2001 to 35% among those in whom it was diagnosed in 2014. This improvement in survival was found across all races and ethnic groups. Similar patterns were found among women with NSCLC. In contrast, mortality from SCLC declined almost entirely as a result of declining incidence, with no improvement in survival. This result correlates with limited treatment advances for SCLC in the time frame we examined., Conclusions: Population-level mortality from NSCLC in the United States fell sharply from 2013 to 2016, and survival after diagnosis improved substantially. Our analysis suggests that a reduction in incidence along with treatment advances - particularly approvals for and use of targeted therapies - is likely to explain the reduction in mortality observed during this period., (Copyright © 2020 Massachusetts Medical Society.)

Background: Risk-prediction models have been proposed to select individuals for lung cancer screening. However, their long-term effects are uncertain. This study evaluates long-term benefits and harms of risk-based screening compared with current United States Preventive Services Task Force (USPSTF) recommendations., Methods: Four independent natural history models were used to perform a comparative modeling study evaluating long-term benefits and harms of selecting individuals for lung cancer screening through risk-prediction models. In total, 363 risk-based screening strategies varying by screening starting and stopping age, risk-prediction model used for eligibility (Bach, PLCOm2012, or Lung Cancer Death Risk Assessment Tool [LCDRAT]), and risk threshold were evaluated for a 1950 US birth cohort. Among the evaluated outcomes were percentage of individuals ever screened, screens required, lung cancer deaths averted, life-years gained, and overdiagnosis., Results: Risk-based screening strategies requiring similar screens among individuals ages 55-80 years as the USPSTF criteria (corresponding risk thresholds: Bach = 2.8%; PLCOm2012 = 1.7%; LCDRAT = 1.7%) averted considerably more lung cancer deaths (Bach = 693; PLCOm2012 = 698; LCDRAT = 696; USPSTF = 613). However, life-years gained were only modestly higher (Bach = 8660; PLCOm2012 = 8862; LCDRAT = 8631; USPSTF = 8590), and risk-based strategies had more overdiagnosed cases (Bach = 149; PLCOm2012 = 147; LCDRAT = 150; USPSTF = 115). Sensitivity analyses suggest excluding individuals with limited life expectancies (<5 years) from screening retains the life-years gained by risk-based screening, while reducing overdiagnosis by more than 65.3%., Conclusions: Risk-based lung cancer screening strategies prevent considerably more lung cancer deaths than current recommendations do. However, they yield modest additional life-years and increased overdiagnosis because of predominantly selecting older individuals. Efficient implementation of risk-based lung cancer screening requires careful consideration of life expectancy for determining optimal individual stopping ages., (© The Author(s) 2019. Published by Oxford University Press.)

Analysis of trends in health data collected over time can be affected by instantaneous changes in coding that cause sudden increases/decreases, or "jumps," in data. Despite these sudden changes, the underlying continuous trends can present valuable information related to the changing risk profile of the population, the introduction of screening, new diagnostic technologies, or other causes. The joinpoint model is a well-established methodology for modeling trends over time using connected linear segments, usually on a logarithmic scale. Joinpoint models that ignore data jumps due to coding changes may produce biased estimates of trends. In this article, we introduce methods to incorporate a sudden discontinuous jump in an otherwise continuous joinpoint model. The size of the jump is either estimated directly (the Joinpoint-Jump model) or estimated using supplementary data (the Joinpoint-Comparability Ratio model). Examples using ICD-9/ICD-10 cause of death coding changes, and coding changes in the staging of cancer illustrate the use of these models.

Background: TAILORx demonstrated that women with node-negative, hormone receptor-positive, HER2-negative breast cancers and Oncotype DX recurrence scores (RS) of 0-25 had similar 9-year outcomes with endocrine vs chemo-endocrine therapy; evidence for women aged 50 years and younger and RS 16-25 was less clear. We estimated how expected changes in practice following the trial might affect US costs in the initial 12 months of care (initial costs)., Methods: Data from Surveillance, Epidemiology, and End Results (SEER), SEER-Medicare, and SEER-Genomic Health Inc datasets were used to estimate Oncotype DX testing and chemotherapy rates and mean initial costs pre- and post-TAILORx (in 2018 dollars), assuming all women received Oncotype DX testing and score-suggested therapy posttrial. Sensitivity analyses tested the impact on costs of assumptions about compliance with testing and score-suggested treatment and estimation methods., Results: Pretrial mean initial costs were $2.816 billion. Posttrial, Oncotype DX testing costs were projected to increase from $115 to $231 million and chemotherapy use to decrease from 25% to 17%, resulting in initial care costs of $2.766 billion, or a net savings of $49 million (1.8% decrease). A small net savings was seen under most assumptions. The one exception was if all women aged 50 years and younger with tumors with RS 16-25 elected to receive chemotherapy, initial care costs could increase by $105 million (4% increase)., Conclusions: Personalizing breast cancer treatment based on tumor genetic profiles could result in small cost decreases in the initial 12 months of care. Studies are needed to evaluate the long-term costs and nonmonetary benefits of personalized cancer care., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

This paper demonstrates the flexibility of a general approach for the analysis of discrete time competing risks data that can accommodate complex data structures, different time scales for different causes, and nonstandard sampling schemes. The data may involve a single data source where all individuals contribute to analyses of both cause-specific hazard functions, overlapping datasets where some individuals contribute to the analysis of the cause-specific hazard function of only one cause while other individuals contribute to analyses of both cause-specific hazard functions, or separate data sources where each individual contributes to the analysis of the cause-specific hazard function of only a single cause. The approach is modularized into estimation and prediction. For the estimation step, the parameters and the variance-covariance matrix can be estimated using widely available software. The prediction step utilizes a generic program with plug-in estimates from the estimation step. The approach is illustrated with three prognostic models for stage IV male oral cancer using different data structures. The first model uses only men with stage IV oral cancer from population-based registry data. The second model strategically extends the cohort to improve the efficiency of the estimates. The third model improves the accuracy for those with a lower risk of other causes of death, by bringing in an independent data source collected under a complex sampling design with additional other-cause covariates. These analyses represent novel extensions of existing methodology, broadly applicable for the development of prognostic models capturing both the cancer and noncancer aspects of a patient's health., (© 2019 John Wiley & Sons, Ltd.)

Background: The American Cancer Society, Centers for Disease Control and Prevention, National Cancer Institute, and North American Association of Central Cancer Registries provide annual updates on cancer occurrence and trends by cancer type, sex, race, ethnicity, and age in the United States. This year's report highlights the cancer burden among men and women age 20-49 years., Methods: Incidence data for the years 1999 to 2015 from the Centers for Disease Control and Prevention- and National Cancer Institute-funded population-based cancer registry programs compiled by the North American Association of Central Cancer Registries and death data for the years 1999 to 2016 from the National Vital Statistics System were used. Trends in age-standardized incidence and death rates, estimated by joinpoint, were expressed as average annual percent change., Results: Overall cancer incidence rates (per 100 000) for all ages during 2011-2015 were 494.3 among male patients and 420.5 among female patients; during the same time period, incidence rates decreased 2.1% (95% confidence interval [CI] = -2.6% to -1.6%) per year in men and were stable in females. Overall cancer death rates (per 100 000) for all ages during 2012-2016 were 193.1 among male patients and 137.7 among female patients. During 2012-2016, overall cancer death rates for all ages decreased 1.8% (95% CI = -1.8% to -1.8%) per year in male patients and 1.4% (95% CI = -1.4% to -1.4%) per year in females. Important changes in trends were stabilization of thyroid cancer incidence rates in women and rapid declines in death rates for melanoma of the skin (both sexes). Among adults age 20-49 years, overall cancer incidence rates were substantially lower among men (115.3 per 100 000) than among women (203.3 per 100 000); cancers with the highest incidence rates (per 100 000) among men were colon and rectum (13.1), testis (10.7), and melanoma of the skin (9.8), and among women were breast (73.2), thyroid (28.4), and melanoma of the skin (14.1). During 2011 to 2015, the incidence of all invasive cancers combined among adults age 20-49 years decreased -0.7% (95% CI = -1.0% to -0.4%) among men and increased among women (1.3%, 95% CI = 0.7% to 1.9%). The death rate for (per 100 000) adults age 20-49 years for all cancer sites combined during 2012 to 2016 was 22.8 among men and 27.1 among women; during the same time period, death rates decreased 2.3% (95% CI = -2.4% to -2.2%) per year among men and 1.7% (95% CI = -1.8% to -1.6%) per year among women., Conclusions: Among people of all ages and ages 20-49 years, favorable as well as unfavorable trends in site-specific cancer incidence were observed, whereas trends in death rates were generally favorable. Characterizing the cancer burden may inform research and cancer-control efforts., (© The Author(s) 2019. Published by Oxford University Press.)

It is fairly common to rank different geographic units, e.g. counties in the USA, based on health indices. In a typical application, point estimates of the health indices are obtained for each county, and the indices are then simply ranked as if they were known constants. Several authors have considered optimal rank estimators under squared error loss on the rank scale as a default method for general purpose ranking, e.g. situations where ranking units across the full spectrum of performance (low, medium, high) is important. While computationally convenient, squared error loss on the rank scale may not represent the true inferential goals of rank consumers. We construct alternative loss functions based on three components: (1) the inferential goal (rank position or pairwise comparisons), (2) the scale (original, log-transformed or rank) and (3) the (positional or pairwise) loss function (0/1, squared error or absolute error). We can obtain optimal ranks for loss functions based on rank positions and nearly optimal ranks for loss functions based on pairwise comparisons paired with highest posterior density (HPD) credible intervals. We compare inferences produced by the various ranking methods, both optimal and heuristic, using low birth weight data for counties in the Midwestern United States, from 2006 to 2012.

Background: National health surveys, such as the National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance System (BRFSS), collect data on cancer screening and smoking-related measures in the US noninstitutionalized population. These surveys are designed to produce reliable estimates at the national and state levels. However, county-level data are often needed for cancer surveillance and related research., Methods: To use the large sample sizes of BRFSS and the high response rates and better coverage of NHIS, we applied multilevel models that combined information from both surveys. We also used relevant sources such as census and administrative records. By using these methods, we generated estimates for several cancer risk factors and screening behaviors that are more precise than design-based estimates., Results: We produced reliable, modeled estimates for 11 outcomes related to smoking and to screening for female breast cancer, cervical cancer, and colorectal cancer. The estimates were produced for 3,112 counties in the United States for the data period from 2008 through 2010., Conclusion: The modeled estimates corrected for potential noncoverage bias and nonresponse bias in the BRFSS and reduced the variability in NHIS estimates that is attributable to small sample size. The small area estimates produced in this study can serve as a useful resource to the cancer surveillance community.

Health disparities are commonplace and of broad interest to policy makers, but are also challenging to measure and communicate. The Health Disparity Calculator software (HD*Calc, v1.2.4) offers Monte Carlo simulation (MCS)-based confidence interval (CI) estimation of eleven disparity measures. The MCS approach provides accurate CI estimation, except when data are scarce (e.g., rare cancers). To address sparse data challenges to CI estimation, we propose two solutions: 1) employing the gamma distribution in the MCS and 2) utilizing a zero-inflated Poisson estimate for Poisson sampling in simulation experiments. We evaluate each solution through simulation studies using female breast, female brain, lung, and cervical cancer data from the Surveillance, Epidemiology, and End Results (SEER) program. We compare the coverage probabilities (CPs) of eleven health disparity measures based on simulated datasets. The truncated normal distribution implemented in the MCS with the standard Poisson samples (the default setting of HD*Calc) leads to less-than-optimal coverage probabilities (<95%). When both the gamma distribution and the estimated mean from the zero-inflated Poisson are used for the MCS, the coverage probabilities are close to the nominal level of 95%. Simulation studies also demonstrate that collapsing age categories for better CI estimation is not a pragmatic solution., Competing Interests: The authors have declared that no competing interests exist.

Cohort or period components of trends can provide a rationale for new research or point to clues on the effectiveness of control strategies. Graphical display of trends guides models that quantify the experience of a population. In this paper, a method for smoothing rates by single year of age and year is developed and displayed to show the contributions of period and cohort to trends. The magnitude of the contribution of period and/or cohort in a model for trends may be assessed by the percentage of deviance explained and the relative contributions of cohort (C) and period (P) individually, known as the C-P score. The method is illustrated using Surveillance, Epidemiology, and End Results data (1975-2014) on lung and bronchial cancer mortality in females and prostate and colorectal cancer incidence in males. Smoothed age-period and age-cohort rates provide a useful first step in studies of etiology and the impact of disease control without imposing a restrictive model. We found that, in this data set, cohort predominates for female lung and bronchial cancer and period predominates for male prostate cancer. However, the effects change with age for male colorectal cancer incidence, indicating an age shift in relevant exposures. These methods are applied on an interactive website for both incidence and mortality at over 20 cancer sites in the United States., (Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2019.)

Spatial correlation raises challenges in estimating confidence intervals for region specific event rates and rate ratios between geographic units that are nested. Methods have been proposed to incorporate spatial correlation by assuming various distributions for the structure of autocorrelation patterns. However, the derivation of these statistics based on approximation may have to condition on the distributional assumption underlying the data generating process, which may not hold for certain situations. This paper explores the feasibility of utilizing a Bayesian convolution model (BCM), which includes an uncorrelated heterogeneity (UH) and a conditional autoregression (CAR) component to accommodate both uncorrelated and correlated spatial heterogeneity, to estimate the 95% confidence intervals for age-adjusted rate ratios among geographic regions with existing spatial correlations. A simulation study is conducted and a BCM method is applied to two cancer incidence datasets to calculate age-adjusted rate/ratio for the counties in the State of Kentucky relative to the entire state. In comparison to three existing methods, without and with spatial correlation, the Bayesian convolution model-based estimation provides moderate shrinkage effect for the point estimates based on the neighbor structure across regions and produces a wider interval due to the inclusion of uncertainty in the spatial autocorrelation parameters. The overall spatial pattern of region incidence rate from BCM approach appears to be like the direct estimates and other methods for both datasets, even though "smoothing" occurs in some local regions. The Bayesian Convolution Model allows flexibility in the specification of risk components and can improve the accuracy of interval estimates of age-adjusted rate ratios among geographical regions as it considers spatial correlation.

Rates of esophageal adenocarcinoma (EAC) have increased rapidly in the United States and much of western Europe, and 5-year survival continues to be poor. 1 Prevention and early detection efforts for EAC have focused on identifying persons with EAC precursor state, Barrett's esophagus, but the survival benefit has been disappointingly low., (Copyright © 2019 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Background: There are significant challenges to the successful conduct of non-inferiority trials because they require large numbers to demonstrate that an alternative intervention is "not too much worse" than the standard. In this paper, we present a novel strategy for designing non-inferiority trials using an approach for determining the appropriate non-inferiority margin (δ), which explicitly balances the benefits of interventions in the two arms of the study (e.g. lower recurrence rate or better survival) with the burden of interventions (e.g. toxicity, pain), and early and late-term morbidity., Methods: We use a decision analytic approach to simulate a trial using a fixed value for the trial outcome of interest (e.g. cancer incidence or recurrence) under the standard intervention (p S ) and systematically varying the incidence of the outcome in the alternative intervention (p A ). The non-inferiority margin, p A - p S  = δ, is reached when the lower event rate of the standard therapy counterbalances the higher event rate but improved morbidity burden of the alternative. We consider the appropriate non-inferiority margin as the tipping point at which the quality-adjusted life-years saved in the two arms are equal., Results: Using the European Polyp Surveillance non-inferiority trial as an example, our decision analytic approach suggests an appropriate non-inferiority margin, defined here as the difference between the two study arms in the 10-year risk of being diagnosed with colorectal cancer, of 0.42% rather than the 0.50% used to design the trial. The size of the non-inferiority margin was smaller for higher assumed burden of colonoscopies., Conclusions: The example demonstrates that applying our proposed method appears feasible in real-world settings and offers the benefits of more explicit and rigorous quantification of the various considerations relevant for determining a non-inferiority margin and associated trial sample size.

Background: Radiotherapy after breast conservation has become the standard of care. Prior meta-analyses on effects of radiotherapy predated availability of gene expression profiling (GEP) to assess recurrence risk and/or did not include all relevant outcomes. This analysis used GEP information with pooled individual-level data to evaluate the impact of omitting radiotherapy on recurrence and mortality., Methods: We considered trials that evaluated or administered radiotherapy after lumpectomy in women with low-risk breast cancer. Women included had undergone lumpectomy and were treated with hormonal therapy for stage I, ER+ and/or PR+, HER2- breast cancer with Oncotype scores no greater than 18. Recurrence-free interval (RFI), type of RFI (locoregional or distant), and breast cancer-specific and overall survival were compared between no radiotherapy and radiotherapy using adjusted Cox models. All statistical tests were two-sided., Results: The final sample included 1778 women from seven trials. Omission of radiotherapy was associated with an overall adjusted hazard ratio of 2.59 (95% confidence interval [CI] = 1.38 to 4.89, P = .003) for RFI. There was a statistically significant increase in any first locoregional recurrence (P = .001), but not distant recurrence events (P = .90), or breast cancer-specific (P = .85) or overall survival (P = .61). Five-year RFI rate was high (93.5% for no radiotherapy vs 97.9% for radiotherapy; absolute reduction = 4.4%, 95% CI = 0.7% to 8.1%, P = .03). The effects of radiotherapy varied across subgroups, with lower RFI rates for those with Oncotype scores of less than 11 (vs 11-18), older (vs younger), and ER+/PR+ status (vs other)., Conclusions: Omission of radiotherapy in hormone-sensitive patients with low recurrence risk may lead to a modest increase in locoregional recurrence event rates, but does not appear to increase the rate of distant recurrence or death.

There is increased interest in eliminating health disparities in the United States and worldwide. Broadly defined, health disparities refer to preventable inequalities in health status, such as cancer to ethnicity, socioeconomic status, gender, education, environment, and geographic locations. To make informed health policy decisions, it is essential to precisely measure the magnitude of disparities and assess trends over time. The Health Disparities Calculator (HD*Calc) is free statistical software that calculates 11 commonly used measures of health disparities and provides corresponding 95% CIs for the 11 measures using either an analytic method or a Monte Carlo simulation-based method; however, the derivation of SEs and coverage properties of the CIs have not been formally evaluated. We used simulation studies to assess the coverage properties of these CIs. We have also conducted bias analyses for measures implemented in HD*Calc using age-adjusted cancer incidence rates from national, state, and county level SEER data. The results of these analyses indicate that HD*Calc should be used with caution to construct CIs for some health disparity measures when the proportion of zero event counts is greater than 25%.

Background: We used two models to simulate a proposed noninferiority trial of radiotherapy (RT) omission in low-risk invasive breast cancer to illustrate how modeling could be used to predict the trial's outcomes, inform trial design, and contribute to practice debates., Methods: The proposed trial was a prospective randomized trial of no-RT vs RT in women age 40 to 74 years undergoing lumpectomy and endocrine therapy for hormone receptor-positive, human epidermal growth factor receptor 2-negative, stage I breast cancer with an Oncotype DX score of 18 or lower. The primary endpoint was recurrence-free interval (RFI), including locoregional recurrence, distant recurrence, and breast cancer death. Noninferiority required the two-sided 90% confidence interval of the RFI hazard ratio (HR) for no-RT vs RT to be entirely below 1.7. Model inputs included published data. The trial was simulated 1000 times, and results were summarized as percent concluding noninferiority and mean (standard deviation) of hazard ratios for Model GE and Model M, respectively., Results: Noninferiority was demonstrated in 18.0% and 3.7% for the two models. The respective means (SD) of the RFI hazard ratios were 1.8 (0.7) and 2.4 (0.9); most were locoregional recurrences. The mean five-year RFI rates for no-RT vs RT (SD) were 92.7% (2.9%) vs 95.5% (2.2%) and 88.4% (2.0%) vs 94.5% (1.6%). Both models showed little or no difference in breast cancer-specific or overall survival. Alternative definitions of low risk based on combinations of age and grade produced similar results., Conclusions: The proposed trial was unlikely to show noninferiority of omitting radiotherapy even using alternative definitions of low-risk, as the endpoint included local recurrence. Future trials regarding radiotherapy should address absolute reduction in recurrence and impact of type of recurrence on the patient.

Regression methodology has been well developed for competing risks data with continuous event times, both for the cause-specific hazard and cumulative incidence functions. However, in many applications, including those from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute, the event times may be observed discretely. Naive application of continuous time regression methods to such data is not appropriate. We propose maximum likelihood inferences for estimation of model parameters for the discrete time cause-specific hazard functions, develop predictions for the associated cumulative incidence functions, and derive consistent variance estimators for the predicted cumulative incidence functions. The methods are readily implemented using standard software for generalized estimating equations, where models for different causes may be fitted separately. For the SEER data, it may be desirable to model different event types on different time scales and the methods are generalized to accommodate such scenarios, extending earlier work on continuous time data. Simulation studies demonstrate that the methods perform well in realistic set-ups. The methodology is illustrated with stage III colon cancer data from SEER., (© 2018, The International Biometric Society.)

Background: Tobacco control efforts implemented in the United States since the 1960s have led to considerable reductions in smoking and smoking-related diseases, including lung cancer., Objective: To project reductions in tobacco use and lung cancer mortality from 2015 to 2065 due to existing tobacco control efforts., Design: Comparative modeling approach using 4 simulation models of the natural history of lung cancer that explicitly relate temporal smoking patterns to lung cancer rates., Setting: U.S. population, 1964 to 2065., Participants: Adults aged 30 to 84 years., Measurements: Models were developed using U.S. data on smoking (1964 to 2015) and lung cancer mortality (1969 to 2010). Each model projected lung cancer mortality by smoking status under the assumption that current decreases in smoking would continue into the future (status quo trends). Sensitivity analyses examined optimistic and pessimistic scenarios., Results: Under the assumption of continued decreases in smoking, age-adjusted lung cancer mortality was projected to decrease by 79% between 2015 and 2065. Concomitantly, and despite the expected growth, aging, and longer life expectancy of the U.S. population, the annual number of lung cancer deaths was projected to decrease from 135 000 to 50 000 (63% reduction). However, 4.4 million deaths from lung cancer are still projected to occur in the United States from 2015 to 2065, with about 20 million adults aged 30 to 84 years continuing to smoke in 2065., Limitation: Projections assumed no changes to tobacco control efforts in the future and did not explicitly consider the potential effect of lung cancer screening., Conclusion: Tobacco control efforts implemented since the 1960s will continue to reduce lung cancer rates well into the next half-century. Additional prevention and cessation efforts will be required to sustain and expand these gains to further reduce the lung cancer burden in the United States., Primary Funding Source: National Cancer Institute.

Background: Temporal trends in prostate cancer incidence and death rates have been attributed to changing patterns of screening and improved treatment (mortality only), among other factors. This study evaluated contemporary national-level trends and their relations with prostate-specific antigen (PSA) testing prevalence and explored trends in incidence according to disease characteristics with stage-specific, delay-adjusted rates., Methods: Joinpoint regression was used to examine changes in delay-adjusted prostate cancer incidence rates from population-based US cancer registries from 2000 to 2014 by age categories, race, and disease characteristics, including stage, PSA, Gleason score, and clinical extension. In addition, the analysis included trends for prostate cancer mortality between 1975 and 2015 by race and the estimation of PSA testing prevalence between 1987 and 2005. The annual percent change was calculated for periods defined by significant trend change points., Results: For all age groups, overall prostate cancer incidence rates declined approximately 6.5% per year from 2007. However, the incidence of distant-stage disease increased from 2010 to 2014. The incidence of disease according to higher PSA levels or Gleason scores at diagnosis did not increase. After years of significant decline (from 1993 to 2013), the overall prostate cancer mortality trend stabilized from 2013 to 2015., Conclusions: After a decline in PSA test usage, there has been an increased burden of late-stage disease, and the decline in prostate cancer mortality has leveled off. Cancer 2018;124:2801-2814. © 2018 American Cancer Society., (© 2018 The Authors. Cancer published by Wiley Periodicals, Inc. on behalf of American Cancer Society.)