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Background: Considerable effort has been expended on tobacco control strategies in the United States since the mid-1950s. However, we have little quantitative information on how changes in smoking behaviors have impacted lung cancer mortality. We quantified the cumulative impact of changes in smoking behaviors that started in the mid-1950s on lung cancer mortality in the United States over the period 1975-2000.Methods: A consortium of six groups of investigators used common inputs consisting of simulated cohort-wise smoking histories for the birth cohorts of 1890 through 1970 and independent models to estimate the number of US lung cancer deaths averted during 1975-2000 as a result of changes in smoking behavior that began in the mid-1950s. We also estimated the number of deaths that could have been averted had tobacco control been completely effective in eliminating smoking after the Surgeon General's first report on Smoking and Health in 1964.Results: Approximately 795,851 US lung cancer deaths were averted during the period 1975-2000: 552,574 among men and 243,277 among women. In the year 2000 alone, approximately 70,218 lung cancer deaths were averted: 44,135 among men and 26,083 among women. However, these numbers are estimated to represent approximately 32% of lung cancer deaths that could have potentially been averted during the period 1975-2000, 38% of the lung cancer deaths that could have been averted in 1991-2000, and 44% of lung cancer deaths that could have been averted in 2000.Conclusions: Our results reflect the cumulative impact of changes in smoking behavior since the 1950s. Despite a large impact of changing smoking behaviors on lung cancer deaths, lung cancer remains a major public health problem. Continued efforts at tobacco control are critical to further reduce the burden of this disease. [ABSTRACT FROM AUTHOR]

Background: A study was undertaken to evaluate the temporal projection methods that are applied by the American Cancer Society to predict 4-year-ahead projections.Methods: Cancer mortality data recorded in each year from 1969 through 2007 for the United States overall and for each state from the National Center for Health Statistics was obtained. Based on the mortality data through 2000, 2001, 2002, and 2003, Projections were made 4 years ahead to estimate the expected number of cancer deaths in 2004, 2005, 2006, 2007, respectively, in the United States and in each state, using 5 projection methods. These predictive estimates were compared to the observed number of deaths that occurred for all cancers combined and 47 cancer sites at the national level, and 21 cancer sites at the state level.Results: Among the models that were compared, the joinpoint regression model with modified Bayesian information criterion selection produced estimates that are closest to the actual number of deaths. Overall, results show the 4-year-ahead projection has larger error than 3-year-ahead projection of death counts when the same method is used. However, 4-year-ahead projection from the new method performed better than the 3-year-ahead projection from the current state-space method.Conclusions: The Joinpoint method with modified Bayesian information criterion model has the smallest error of all the models considered for 4-year-ahead projection of cancer deaths to the current year for the United States overall and for each state. This method will be used by the American Cancer Society to project the number of cancer deaths starting in 2012. [ABSTRACT FROM AUTHOR]

Background: Current estimates of the costs of cancer care in the United States are based on data from 2003 and earlier. However, incidence, survival, and practice patterns have been changing for the majority of cancers.Methods: Cancer prevalence was estimated and projected by phase of care (initial year following diagnosis, continuing, and last year of life) and tumor site for 13 cancers in men and 16 cancers in women through 2020. Cancer prevalence was calculated from cancer incidence and survival models estimated from Surveillance, Epidemiology, and End Results (SEER) Program data. Annualized net costs were estimated from recent SEER-Medicare linkage data, which included claims through 2006 among beneficiaries aged 65 years and older with a cancer diagnosis. Control subjects without cancer were identified from a 5% random sample of all Medicare beneficiaries residing in the SEER areas to adjust for expenditures not related to cancer. All cost estimates were adjusted to 2010 dollars. Different scenarios for assumptions about future trends in incidence, survival, and cost were assessed with sensitivity analysis.Results: Assuming constant incidence, survival, and cost, we projected 13.8 and 18.1 million cancer survivors in 2010 and 2020, respectively, with associated costs of cancer care of 124.57 and 157.77 billion 2010 US dollars. This 27% increase in medical costs reflects US population changes only. The largest increases were in the continuing phase of care for prostate cancer (42%) and female breast cancer (32%). Projections of current trends in incidence (declining) and survival (increasing) had small effects on 2020 estimates. However, if costs of care increase annually by 2% in the initial and last year of life phases of care, the total cost in 2020 is projected to be $173 billion, which represents a 39% increase from 2010.Conclusions: The national cost of cancer care is substantial and expected to increase because of population changes alone. Our findings have implications for policy makers in planning and allocation of resources. [ABSTRACT FROM AUTHOR]

Trends in incidence or mortality rates over a specified time interval are usually described by the conventional annual per cent change (cAPC), under the assumption of a constant rate of change. When this assumption does not hold over the entire time interval, the trend may be characterized using the annual per cent changes from segmented analysis (sAPCs). This approach assumes that the change in rates is constant over each time partition defined by the transition points, but varies among different time partitions. Different groups (e.g. racial subgroups), however, may have different transition points and thus different time partitions over which they have constant rates of change, making comparison of sAPCs problematic across groups over a common time interval of interest (e.g. the past 10 years). We propose a new measure, the average annual per cent change (AAPC), which uses sAPCs to summarize and compare trends for a specific time period. The advantage of the proposed AAPC is that it takes into account the trend transitions, whereas cAPC does not and can lead to erroneous conclusions. In addition, when the trend is constant over the entire time interval of interest, the AAPC has the advantage of reducing to both cAPC and sAPC. Moreover, because the estimated AAPC is based on the segmented analysis over the entire data series, any selected subinterval within a single time partition will yield the same AAPC estimate--that is it will be equal to the estimated sAPC for that time partition. The cAPC, however, is re-estimated using data only from that selected subinterval; thus, its estimate may be sensitive to the subinterval selected. The AAPC estimation has been incorporated into the segmented regression (free) software Joinpoint, which is used by many registries throughout the world for characterizing trends in cancer rates. [ABSTRACT FROM AUTHOR]

Background: Cancer incidence rates and trends are a measure of the cancer burden in the general population. We studied the impact of reporting delay and reporting error on incidence rates and trends for cancers of the female breast, colorectal, lung/bronchus, prostate, and melanoma.Methods: Based on statistical models, we obtained reporting-adjusted (i.e., adjusted for both reporting delay and reporting error) case counts for each diagnosis year beginning in 1981 using reporting information for patients diagnosed with cancer in 1981-1998 from nine cancer registries that participate in the Surveillance, Epidemiology, and End Results (SEER) program. Joinpoint linear regression was used for trend analysis. All statistical tests are two-sided.Results: Initial incidence case counts (i.e., after the standard 2-year delay) accounted for only 88%-97% of the estimated final counts; it would take 4-17 years for 99% or more of the cancer cases to be reported. The percent change between reporting-adjusted and unadjusted cancer incidence rates for the 1998 diagnosis year ranged from 3% for colorectal cancers to 14% for melanoma in whites and for prostate cancer in black males. Reporting-adjusted current incidence trends for breast cancer and lung/bronchus cancer in white females showed statistically significant increases (estimated annual percent change [EAPC] = 0.6%, 95% confidence interval [CI] = 0.1% to 1.2%) and 1.2%, 95% CI = 0.7% to 1.6%, respectively), whereas trends for these cancers using unadjusted incidence rates were not statistically significantly different from zero (EAPC = 0.4%, 95% CI = -0.1% to 0.9% and 0.5%, 95% CI = -0.1% to 1.1%, respectively). Reporting-adjusted melanoma incidence rates for white males showed a statistically significant increase since 1981 (EAPC = 4.1%, 95% CI = 3.8% to 4.4%) in contrast to the unadjusted incidence rate, which was most consistent with a flat or downward trend (EAPC = -4.2%, 95% CI = -11.1% to 3.3%) after 1996.Conclusions: Reporting-adjusted cancer incidence rates are valuable in precisely determining current cancer incidence rates and trends and in monitoring the timeliness of data collection. Ignoring reporting delay and reporting error may produce downwardly biased cancer incidence trends, particularly in the most recent diagnosis years. [ABSTRACT FROM AUTHOR]

Background: Overdiagnosis of clinically insignificant prostate cancer is considered a major potential drawback of prostate-specific antigen (PSA) screening. Quantitative estimates of the magnitude of this problem are, however, lacking. We estimated rates of prostate cancer overdiagnosis due to PSA testing that are consistent with the observed incidence of prostate cancer in the United States from 1988 through 1998. Overdiagnosis was defined as the detection of prostate cancer through PSA testing that otherwise would not have been diagnosed within the patient's lifetime.Methods: We developed a computer simulation model of PSA testing and subsequent prostate cancer diagnosis and death from prostate cancer among a hypothetical cohort of two million men who were 60-84 years old in 1988. Given values for the expected lead time--that is, the time by which the test advanced diagnosis--and the expected incidence of prostate cancer in the absence of PSA testing, the model projected the increase in population incidence of prostate cancer associated with PSA testing. By comparing the model-projected incidence with the observed incidence derived from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) registry data, we determined the lead times and corresponding overdiagnosis rates that were consistent with the observed data.Results: SEER data on prostate cancer incidence from 1988 through 1998 were consistent with overdiagnosis rates of approximately 29% for whites and 44% for blacks among men with prostate cancers detected by PSA screening.Conclusions: Among men with prostate cancer that would be detected only at autopsy, these rates correspond to overdiagnosis rates of, at most, 15% in whites and 37% in blacks. The observed trends in prostate cancer incidence are consistent with considerable overdiagnosis among PSA-detected cases. However, the results suggest that the majority of screen-detected cancers diagnosed between 1988 and 1998 would have presented clinically and that only a minority of cases found at autopsy would have been detected by PSA testing. [ABSTRACT FROM AUTHOR]

Background: The Connecticut Tumor Registry (CTR) has collected cancer data for a sufficiently long period of time to capture essentially all prevalent cases of cancer, and to provide unbiased estimates of cancer prevalence. However, prevalence proportions estimated from Connecticut data may not be representative of the total US, particularly for racial/ethnic subgroups. The purpose of this study is to apply the modelling approach developed by Capocaccia and De Angelis to cancer data from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute to obtain more representative US site-specific cancer prevalence proportion estimates for white and black patients.Methods: Incidence and relative survival were modelled and used to obtain estimated completeness indices of SEER prevalence proportions for all cancer sites combined, stomach, cervix uteri, skin melanomas, non-Hodgkin's lymphomas, lung and bronchus, colon/rectum, female breast, and prostate. For validation purposes, modelled completeness indices were computed for Connecticut and compared with empirical completeness indices (the ratio of Connecticut based prevalence proportion estimates using 1973-1993 data to 1940-1993 data). The SEER-based modelled completeness indices were used to adjust SEER prevalence proportion estimates for white and black patients.Results: Model validation showed that the adjusted SEER cancer prevalence proportions provided reasonably unbiased prevalence proportion estimates in general, although more complex modelling of the completeness indices is necessary for female cancers of the colon, melanoma, breast, cervix, and all cancers combined. The SEER-based cancer prevalence proportions are incomplete for most cancer sites, more so for women, whites, and at older ages. For all cancers combined, prevalence proportions tended to be higher for whites than blacks. For the site-specific cancers this was true for stomach, prostate, cervix uteri, and lung and bronchus (men only). For colon/rectal cancers the prevalence proportions were higher for blacks through ages 59 (men) and 64 (women), and then for the remaining ages they were higher for whites. Prevalence proportions were lowest for stomach cancer and highest for prostate and female breast cancers. Men experienced higher prevalence proportions than women for skin melanomas, non-Hodgkin's lymphomas, lung and bronchus, and colon/rectal cancers.Conclusion: The modelling approach applied to SEER data generally provided reasonable estimates of cancer prevalence. These estimates are useful because they are more representative of cancer prevalence than previously obtained and reported in the US. [ABSTRACT FROM AUTHOR]

Objective: To compare breast cancer screening rates from the 1991 survey with data from 1987-88 for women aged 65 to 74.Design: Surveys of women from five communities.Settings: Five control communities of the National Cancer Institute's Breast Cancer Screening Consortium.Participants: White, non-Hispanic women, ages 65 to 74; 499 in 1987-88 and 2156 in 1991. Response rates for the first survey wave ranged by area from 65% to 77% and for the second survey wave, from 62% to 85%.Main Outcome Measure: Mammogram and clinical breast examination during the past year and performance of monthly breast self examination, with the screening rates in wave 2 directly standardized to the income and education distribution of wave 1 in each area.Results: Mammography use between waves increased significantly (P < 0.05 after adjusting for education, income, and age) in all but one area (from 19% to 33% in wave 1 to 35% to 59% in wave 2). Among women who had a mammogram, the percent who also had a clinical breast examination decreased between waves from 95% to 85% (P = 0.001).Conclusions: Mammography in older women increased dramatically over 3 years, although the use of clinical breast examination may be decreasing. [ABSTRACT FROM AUTHOR]

Background: The lifetime risk of developing breast cancer in U.S. women, often quoted as one in nine, is a commonly cited cancer statistic. However, many estimates have used cancer rates derived from total rather than the cancer-free population and have not properly accounted for multiple cancers in the same individual.Purpose: Our purpose was to provide a revised method for calculating estimates of the lifetime risk of developing breast cancer and to aid in interpretation of the estimates.Methods: A multiple decrement life table was derived by applying age-specific incidence and mortality rates from cross-sectional data to a hypothetical cohort of women. Incidence, mortality, and population data from 1975-1988 were used, representing the geographic areas of the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program. The incidence rates reflected only the first breast primary cancer; mortality rates reflected causes other than breast cancer. The population denominator used in calculating incidence rates was adjusted to reflect only those women without previously diagnosed breast cancers in the hypothetical cohort.Results: Our calculations showed an overall lifetime risk for developing invasive breast cancer of approximately one in eight with use of 1987-1988 SEER data, although up to age 85, it was still the commonly quoted one in nine.Conclusion: Our estimate was calculated assuming constant age-specific rates derived from 1987-1988 SEER data. Because incidence and mortality rates change over time, conditional risk estimates over the short term (10 or 20 years) may be more reliable. A large portion of the rise in the lifetime risk of breast cancer estimated using 1975-1977 data (one in 10.6) to an estimate using 1987-1988 data (one in eight) may be attributed to 1) early detection of prevalent cases due to increased use of mammographic screening and 2) lower mortality due to causes other than breast cancer. A common misperception is that the lifetime risk estimate assumes that all women live to a particular age (e.g., 85 or 95). In fact, the calculation assumes that women can die from causes other than breast cancer at any possible age. Cutting off the lifetime risk calculation at age 85 assumes that no women develop breast cancer after that age. While the lifetime risk of developing breast cancer rose over the period 1976-1977 to 1987-1988, the lifetime risk of dying of breast cancer increased from one in 30 to one in 28, reflecting generally flat mortality trends. [ABSTRACT FROM AUTHOR]

OBJECTIVES: This article presents a space-time scan statistic, useful for evaluating space-time cluster alarms, and illustrates the method on a recent brain cancer cluster alarms in Los Alamos, NM. METHODS: The space-time scan statistic accounts for the preselection bias and multiple testing inherent in a cluster alarm. Confounders and time trends can be adjusted for. RESULTS: The observed excess of brain cancer in Los Alamos was not statistically significant. CONCLUSIONS: The space-time scan statistic is useful as a screening tool for evaluating which cluster alarms merit further investigation and which clusters are probably chance occurrences. [ABSTRACT FROM AUTHOR]

Background: We used modeling techniques to assess the relative and absolute contributions of screening mammography and adjuvant treatment to the reduction in breast-cancer mortality in the United States from 1975 to 2000. Methods: A consortium of investigators developed seven independent statistical models of breast-cancer incidence and mortality. All seven groups used the same sources to obtain data on the use of screening mammography, adjuvant treatment, and benefits of treatment with respect to the rate of death from breast cancer. Results: The proportion of the total reduction in the rate of death from breast cancer attributed to screening varied in the seven models from 28 to 65 percent (median, 46 percent), with adjuvant treatment contributing the rest. The variability across models in the absolute contribution of screening was larger than it was for treatment, reflecting the greater uncertainty associated with estimating the benefit of screening. Conclusions: Seven statistical models showed that both screening mammography and treatment have helped reduce the rate of death from breast cancer in the United States. [ABSTRACT FROM AUTHOR]

CP*Trends is a widely used SEER website used to explore temporal effects of period and cohort on cancer incidence and mortality. It provides a graphical display of smoothed rates, and a C-P Score that helps to assess the magnitude of the effect of cohort and period. This update provides results for African Americans and Whites. The C-P Score has an intrinsic bias favoring cohort because there are many more cohorts than periods. An adjusted C-P Score removes some of this advantage. Bootstrap confidence intervals are given, which allow one to see the effects of different sample sizes on the model results. Finally, users may control window size used in the smoothing algorithm, which helps to avoid over smoothing or masking of trends. The method is illustrated using data on cervical cancer incidence trends for African Americans and Whites, 1975-2018. Rates are higher for African Americans, and both races have contributions for cohort. However, the period effect is only strongly evident in Whites. Visual inspection of White trends suggests possible differences for those older and younger than age 50. These methods are applied in an interactive website displaying incidence and mortality trends for over 20 cancer sites in the US., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

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).

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.