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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. [ABSTRACT FROM AUTHOR]

Joinpoint regression can model trends in time-specific estimates from aggregated data. These methods have been developed mainly for nonsurvey data such as cancer registry data assuming that the time-specific estimates are uncorrelated from time point to time point. This independence assumption can be violated for trends in time-specific estimates from complex survey samples due to using the same primary sampling units across time and, therefore, the full variance–covariance matrix of the time-specific estimates should be incorporated into the regression model fitting. This article extends these joinpoint methods for analyzing complex survey data within the National Cancer Institute's Joinpoint software and empirically compares the extended method to existing methods for analyses of time trends in three surveys. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The National Cancer Institute's Surveillance, Epidemiology, and End Results Program releases research files of cancer registry data. These files include geographic information at the county level, but no finer. Access to finer geography, such as census tract identifiers, would enable richer analyses--for example, examination of health disparities across neighborhoods. To date, tract identifiers have been left off the research files because they could compromise the confidentiality of patients' identities. We present an approach to inclusion of tract identifiers based on multiply imputed, synthetic data. The idea is to build a predictive model of tract locations, given patient and tumor characteristics, and randomly simulate the tract of each patient by sampling from this model. For the predictive model, we use multivariate regression trees fitted to the latitude and longitude of the population centroid of each tract. We implement the approach in the registry data from California. The method results in synthetic data that reproduce a wide range (but not all) of analyses of census tract socioeconomic cancer disparities and have relatively low disclosure risks, which we assess by comparing individual patients' actual and synthetic tract locations. We conclude with a discussion of how synthetic data sets can be used by researchers with cancer registry data. [ABSTRACT FROM AUTHOR]

In the standard analysis of competing risks data, proportional hazards models are fit to the cause-specific hazard functions for all causes on the same time scale. These regression analyses are the foundation for predictions of cause-specific cumulative incidence functions based on combining the estimated cause-specific hazard functions. However, in predictions arising from disease registries, where only subjects with disease enter the database, disease-related mortality may be more naturally modeled on the time since diagnosis time scale while death from other causes may be more naturally modeled on the age time scale. The single time scale methodology may be biased if an incorrect time scale is employed for one of the causes and an alternative methodology is not available. We propose inferences for the cumulative incidence function in which regression models for the cause-specific hazard functions may be specified on different time scales. Using the disease registry data, the analysis of other cause mortality on the age scale requires left truncating the event time at the age of disease diagnosis, complicating the analysis. In addition, standard Martingale theory is not applicable when combining regression models on different time scales. We establish that the covariate conditional predictions are consistent and asymptotically normal using empirical process techniques and propose consistent variance estimators for constructing confidence intervals. Simulation studies show that the proposed two time scales method performs well, outperforming the single time-scale predictions when the time scale is misspecified. The methods are illustrated with stage III colon cancer data obtained from the Surveillance, Epidemiology, and End Results program of National Cancer Institute. [ABSTRACT FROM AUTHOR]

Cancer overdiagnosis is frequently estimated using the excess incidence in a screened group relative to that in an unscreened group. However, conditions for unbiased estimation are poorly understood. We developed a mathematical framework to project the effects of screening on the incidence of relevant cancers--that is, cancers that would present clinically without screening. Screening advances the date of diagnosis for a fraction of preclinical relevant cancers. Which diagnoses are advanced and by how much depends on the preclinical detectable period, test sensitivity, and screening patterns. Using the model, we projected incidence in common trial designs and population settings and compared excess incidence with true overdiagnosis. In trials with no control arm screening, unbiased estimates are available using cumulative incidence if the screen arm stops screening and using annual incidence if the screen arm continues screening. In both designs, unbiased estimation requires waiting until screening stabilizes plus the maximum preclinical period. In continued-screen trials and population settings, excess cumulative incidence is persistently biased. We investigated this bias in published estimates from the European Randomized Study of Screening for Prostate Cancer after 9-13 years. In conclusion, no trial or population setting automatically permits unbiased estimation of overdiagnosis; sufficient follow-up and appropriate analysis remain crucial. [ABSTRACT FROM AUTHOR]

Background Treatment options for colorectal cancer (CRC) have improved substantially over the past 25 years. Measuring the impact of these improvements on survival outcomes is challenging, however, against the background of overall survival gains from advancements in the prevention, screening, and treatment of other conditions. Relative survival is a metric that accounts for these concurrent changes, allowing assessment of changes in CRC survival. We describe stage- and location-specific trends in relative survival after CRC diagnosis. Methods We analyzed survival outcomes for 233965 people in the Surveillance Epidemiology and End Results (SEER) program who were diagnosed with CRC between January 1, 1975, and December 31, 2003. All models were adjusted for sex, race (black vs white), age at diagnosis, time since diagnosis, and diagnosis year. We estimated the proportional difference in survival for CRC patients compared with overall survival for age-, sex-, race-, and period-matched controls to account for concurrent changes in overall survival using two-sided Wald tests. Results We found statistically significant reductions in excess hazard of mortality from CRC in 2003 relative to 1975, with excess hazard ratios ranging from 0.75 (stage IV colon cancer; P < .001) to 0.32 (stage I rectal cancer; P < .001), indicating improvements in relative survival for all stages and cancer locations. These improvements occurred in earlier years for patients diagnosed with stage I cancers, with smaller but continuing improvements for later-stage cancers. Conclusions Our results demonstrate a steady trend toward improved relative survival for CRC, indicating that treatment and surveillance improvements have had an impact at the population level. [ABSTRACT FROM PUBLISHER]

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 PUBLISHER]

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]

In the USA cancer as a whole is the second leading cause of death and a major burden to health care; thus medical progress against cancer is a major public health goal. There are many individual studies to suggest that cancer treatment breakthroughs and early diagnosis have significantly improved the prognosis of cancer patients. To understand better the relationship between medical improvements and the survival experience for the patient population at large, it is useful to evaluate cancer survival trends on the population level, e.g. to find out when and how much the cancer survival rates changed. We analyse population-based grouped cancer survival data by incorporating join points into the survival models. A join point survival model facilitates the identification of trends with significant change-points in cancer survival, when related to cancer treatments or interventions. The Bayesian information criterion is used to select the number of join points. The performance of the join point survival models is evaluated with respect to cancer prognosis, join point locations, annual percentage changes in death rates by year of diagnosis and sample sizes through intensive simulation studies. The model is then applied to grouped relative survival data for several major cancer sites from the ‘Surveillance, epidemiology and end results’ programme of the National Cancer Institute. The change-points in the survival trends for several major cancer sites are identified and the potential driving forces behind such change-points are discussed. [ABSTRACT FROM AUTHOR]

Background. A model that predicts the economic benefit of reduced cancer mortality provides critical information for allocating scarce resources to the interventions with the greatest benefits. Method.s We developed models using the human capital approach, which relies on earnings as a measure of productivity, to estimate the value of productivity lost as a result of cancer mortality. The base model aggregated age and sex-specific data from four primary sources: 1) the US Bureau of the Census, 2) US death certificate data for 1999-2003, 3) cohort life tables from the Berkeley Mortality Database for 1900-2000, and 4) the Bureau of Labor Statistics Current Population Survey. In a model that included costs of caregiving and household work, data from the National Human Activity Pattern Survey and the Caregiving in the U.S. study were used. Sensitivity analyses were performed using six types of cancer assuming a 1% decline in cancer mortality rates. The values of forgone earnings for employed individuals and imputed forgone earnings for informal caregiving were then estimated for the years 2000-2020. Results. The annual productivity cost from cancer mortality in the base model was approximately $115.8 billion in 2000; the projected value was $147.6 billion for 2020. Death from lung cancer accounted for more than 27% of productivity costs. A 1% annual reduction in lung, colorectal, breast, leukemia, pancreatic, and brain cancer mortality lowered productivity costs by $814 million per year. Including imputed earnings lost due to caregiving and household activity increased the base model total productivity cost to $232.4 billion in 2000 and to $308 billion in 2020. Conclusions. Investments in programs that target the cancers with high incidence and/or cancers that occur in younger, working-age individuals are likely to yield the greatest reductions in productivity losses to society. [ABSTRACT FROM AUTHOR]

Background.Value-of-life methods are increasingly used in policy analyses of the economic burden of disease. The purpose of this study was to estimate and project the value of life lost from cancer deaths in the United States. Methods.We estimated and projected US age-specific mortality rates for all cancers and for 16 types of cancer in men and 18 cancers in women in the years 2000-2020 and applied them to US population projections to estimate the number of deaths in each year. Cohort life tables were used to calculate the remaining life expectancy in the absence of cancer deaths-the person-years of life lost (PYLL). We used a willingnessto-pay approach in which the value of life lost due to cancer death was calculated by multiplying PYLL by an estimate of the value of 1 year of life ($150000). We performed sensitivity analyses for female breast, colorectal, lung, and prostate cancers using varying assumptions about future cancer mortality rates through the year 2020. Results.The value of life lost from all cancer deaths in the year 2000 was $960.6 billion; lung cancer alone represented more than 25% of this value. Projections for the year 2020 with current cancer mortality rates showed a 53% increase in the total value of life lost ($1472.5 billion). Projected annual decreases of cancer mortality rates of 2% reduced the expected value of life lost in the year 2020 from $121.0 billion to $80.7 billion for breast cancer, $140.1 billion to $93.5 billion for colorectal cancer, from $433.4 billion to $289.4 billion for lung cancer, and from $58.4 billion to $39.0 billion for prostate cancer. Conclusions.Estimated value of life lost due to cancer deaths in the United States is substantial and expected to increase dramatically, even if mortality rates remain constant, because of expected population changes. These estimates and projections may help target investments in cancer control strategies to tumor sites that are likely to result in the greatest burden of disease and to interventions that are the most cost-effective. [ABSTRACT FROM AUTHOR]

The CISNET breast cancer program is a consortium of seven research groups modeling the impact of various cancer interventions on the national trends of breast cancer incidence and mortality. Each of the modeling groups participated in a CISNET breast cancer base case analysis with the objective of assessing the impact of mammography and adjuvant therapy on breast cancer mortality between 1975 and 2000. The comparative modeling approach used to address this question allowed for a unique view into the process of modeling. Results shown here expand on those recently reported in the New England Journal of Medicine (Berry et al., N Engl J Med 2005;353:1784-92) by presenting mortality impact in several different ways to facilitate comparisons between models. Comparisons of each group's results in the context of modeling assumptions made during the process gave insight into how specific model assumptions may have affected the results. The median estimate for the percent decline in breast cancer mortality due to mammography was 15% (range of 8%-23%), and the median estimate for the percent decline in mortality due to adjuvant treatment was 19% (range of 12%-21%). A detailed discussion of the differences in modeling approaches and how those differences may have influenced the mortality results concludes the chapter. [ABSTRACT FROM AUTHOR]

Background: Clinical trials have shown tamoxifen to be effective only in women with estrogen receptor (ER)-positive tumors, in a previous model, trends in the utilization of adjuvant therapy were modeled only as a function of age and stage of the disease and not ER status. In this paper, we integrate this previous estimate on the use of adjuvant systemic therapy for breast cancer in the United States with information on ER status from the Patterns of Care (POC) data to estimate the dissemination of adjuvant therapy for women with different ER-status tumors. We also summarize efficacy of adjuvant systemic therapy reported in the over- views of early breast cancer clinical trials. These two inputs, dissemination and efficacy, are key pieces for models that investigate the effect of breast cancer adjuvant therapy on the decline of U.S. breast cancer mortality. Methods: The adjustments to the previous models are calculated using the POC data on 7116 women with breast cancer diagnosed from 1987 to 1991 and in 1995 who were randomly selected from the Surveillance, and Epidemiology, and End Results (SEER) program registries. The POC data provide more accurate information on treatment and clinical variables (e. g., ER status) than the SEER data because medical records are reabstracted and further verified with treating physicians. Results: Use of multiagent chemotherapy is higher for younger women, (<50 years) and for women whose tumors were shown to be ER negative or borderline. The use of tamoxifen is higher among older women and women with ER-positive tumors. After 1980 the combined use of multiagent chemotherapy and tamoxifen for women diagnosed with breast cancer at ages 69 or younger increased more for women whose tumors were ER status positive or unknown than ER status negative. Older women (>69 years) seem to receive almost exclusively tamoxifen irrespective of ER status, except for a small percentage of those with more advanced stages (II- and II+/IIIA) who also receive multiagent chemotherapy. Discussion: The estimated dissemination trends by ER status, based on modeling the POC data, reveal that treatment strategies with demonstrated efficacy in clinical trials have been adopted into practice. The dissemination and efficacy are the two factors necessary to input into models to determine the population impact of these therapies on U.S. breast cancer mortality. The largest decline in mortality would be expected for younger women (<60 years) with ER-positive tumors or whose tumors are of unknown status because of the largest efficacy and dissemination of adjuvant therapy in this group. [ABSTRACT FROM AUTHOR]

The article reports on the Cancer Intervention and Surveillance Modeling Network's (CISNET) investigation of the impact of adjuvant therapy and screening mammography on breast cancer mortality in the United States between 1975 and 2000. CISNET is a consortium whose focus is modeling the impact of cancer control interventions on population trends in incidence and mortality for breast cancer. The results of these investigations are highlighted in the article.

The article discusses the works of the Cancer Intervention and Surveillance Modeling Network (CISNET) breast group in the United States. The authors analyzes the aims, results and programs initiated by CISNET to better study the mortality trends and potential treatment methods for breast cancer. The implication of the CISNET programs on women's health are also discussed.

The CISNET Breast Cancer program is a National Cancer Institute-sponsored collaboration composed of seven research groups that have modeled the impact of screening and adjuvant treatment on trends in breast cancer incidence and mortality over the period 1975-2000 (base case). This collaboration created a unique opportunity to make direct comparison of results from different models of population- based cancer screening produced in response to the same question. Comparing results in all but the most cursory way necessitates comparison of the models themselves. Previous chapters have discussed the models individual in detail. This chapter will aid the reader in understanding key areas of difference between the models. A focused analysis of differences and similarities between the models is presented with special attention paid to areas deemed most likely to contribute substantially to the results of the target analysis. [ABSTRACT FROM AUTHOR]

In estimating the impact of mammography and adjuvant treatment on U.S. breast cancer mortality rates, several parameters were common to all the Cancer Intervention and Surveillance Modeling Network (CISNET) models participating in the breast cancer base case. Models either used the parameters directly as input or calibrated their models to reproduce the common set of parameters. This chapter describes the common input parameters that are not specifically discussed elsewhere in the monograph. [ABSTRACT FROM AUTHOR]

Incidence rates for breast cancer in U.S. women have steadily increased for decades, but the reasons are not well understood. A recent upturn in these trends suggests that one component may be the effect of more aggressive screening in the population. The age-period-cohort framework, in which the temporal components associated with year of diagnosis and generation are evaluated, can assist in interpreting the elements associated with these trends. A unique approach for exploring other ways of partitioning the contribution of the different temporal components is described and applied to breast cancer incidence data (ICDO 174.0-174.9) from the Surveillance, Epidemiology and End Results (SEER) registries. Single-year intervals for age and year of diagnosis were used to fit models that provide estimates of the trends associated with the individual temporal elements. A log-linear model for age, period, and cohort was fitted using Poisson regression, and estimates of the separate time trends were calculated. The trends with period increased after 1982, when more aggressive screening began, and the trend is steeper for women older than 40 years. Cohort trends have increased steadily, although recent cohorts appear to be somewhat flat for women aged 50 years or younger, whereas the trend for those older than 50 years have continued to increase. Estimates of cohort trends in rates are also provided by extrapolating what would have occurred had there been no period trend before or after 1982, thus providing an estimate of the magnitude of the upturn that occurred after the recent emphasis on screening. [ABSTRACT FROM AUTHOR]

The annual percent change (APC) is often used to measure trends in disease and mortality rates, and a common estimator of this parameter uses a linear model on the log of the age-standardized rates. Under the assumption of linearity on the log scale, which is equivalent to a constant change assumption, APC can be equivalently defined in three ways as transformations of either (1) the slope of the line that runs through the log of each rate, (2) the ratio of the last rate to the first rate in the series, or (3) the geometric mean of the proportional changes in the rates over the series. When the constant change assumption fails then the first definition cannot be applied as is, while the second and third definitions unambiguously define the same parameter regardless of whether the assumption holds. We call this parameter the percent change annualized (PCA) and propose two new estimators of it. The first, the two-point estimator, uses only the first and last rates, assuming nothing about the rates in between. This estimator requires fewer assumptions and is asymptotically unbiased as the size of the population gets large, but has more variability since it uses no information from the middle rates. The second estimator is an adaptive one and equals the linear model estimator with a high probability when the rates are not significantly different from linear on the log scale, but includes fewer points if there are significant departures from that linearity. For the two-point estimator we can use confidence intervals previously developed for ratios of directly standardized rates. For the adaptive estimator, we show through simulation that the bootstrap confidence intervals give appropriate coverage. [ABSTRACT FROM AUTHOR]

The method of Bayesian model selection for join point regression models is developed. Given a set of K+1 join point models M0, M1, ..., M K with 0, 1, ..., K join points respec-tively, the posterior distributions of the parameters and competing models M k are computed by Markov chain Monte Carlo simulations. The Bayes information criterion BIC is used to select the model M k with the smallest value of BIC as the best model. Another approach based on the Bayes factor selects the model M k with the largest posterior probability as the best model when the prior distribution of M k is discrete uniform. Both methods are applied to analyse the observed US cancer incidence rates for some selected cancer sites. The graphs of the join point models fitted to the data are produced by using the methods proposed and compared with the method of Kim and co-workers that is based on a series of permutation tests. The analyses show that the Bayes factor is sensitive to the prior specification of the variance σ2, and that the model which is selected by BIC fits the data as well as the model that is selected by the permutation test and has the advantage of producing the posterior distribution for the join points. The Bayesian join point model and model selection method that are presented here will be integrated in the National Cancer Institute's join point software ( ) and will be available to the public. [ABSTRACT FROM AUTHOR]

Segmented line regression models, which are composed of continuous linear phases, have been applied to describe changes in rate trend patterns. In this article, we propose a procedure to compare two segmented line regression functions, specifically to test (i) whether the two segmented line regression functions are identical or (ii) whether the two mean functions are parallel allowing different intercepts. A general form of the test statistic is described and then the permutation procedure is proposed to estimate the p-value of the test. The permutation test is compared to an approximateF-test in terms of the p-value estimation and the performance of the permutation test is studied via simulations. The tests are applied to compare female lung cancer mortality rates between two registry areas and also to compare female breast cancer mortality rates between two states. [ABSTRACT FROM AUTHOR]

Ecologic studies of cancer screening examine cancer mortality rates in relation to use of population screening. These studies can be confounded by treatment patterns or influenced by choice of outcome and time horizon. Interpretation can be complicated by uncertainty about when mortality differences might be expected. The authors examined these issues in an ecologic analysis of prostate-specific antigen (PSA) screening and prostate cancer mortality across nine cancer registries in the United States. Results suggested a weak trend for areas with greater PSA screening rates to have greater declines in prostate cancer mortality; however, the magnitude of this trend varied considerably with the time horizon and outcome measure. A computer model was used to determine whether divergence of mortality declines would be expected under an assumption of a clinically significant survival benefit due to screening. Given a mean lead time of 5 years, the model projected that differences in mortality between high- and low-use areas should be apparent by 1999 in the absence of other factors affecting mortality. The authors concluded that modest differences in PSA screening rates across areas, together with additional sources of variation, could have produced a negative ecologic result. Ecologic analyses of the effectiveness of PSA testing should be interpreted with caution. [ABSTRACT FROM AUTHOR]

Background: Understanding trends in the dissemination of findings from clinical research can help in estimating their population-level benefits. We evaluated trends in the use of adjuvant multi-agent chemotherapy, tamoxifen, and the combination of both treatments for early-stage breast cancer in the United States from 1975 through 1999.Methods: Data on treatment of 217 508 patients diagnosed from 1975 through 1999 with stages I, II, and IIIA breast cancer were obtained from eight registries of the Surveillance, Epidemiology, and End Results (SEER) Program. Models of dissemination were developed from these data after adjustment based on information from a series of population-based Patterns of Care (POC) studies that randomly selected case patients from the SEER registries. The POC studies included 7116 patients diagnosed from 1987 through 1991 and in 1995 who were eliminated from the SEER data used in this analysis.Results: The modeled disseminations were generally compatible with the POC-observed proportions of each treatment. The use of multi-agent chemotherapy was higher among premenopausal women, and the use of tamoxifen was higher among postmenopausal women. The use of multi-agent chemotherapy for postmenopausal women diagnosed with lymph node-positive stage II+ or stage IIIA cancer reached a peak in 1983 and then decreased through 1986, indicating its substitution with tamoxifen. After 1986, the combined use of multi-agent chemotherapy and tamoxifen increased for almost all stages and ages. After the early 1990s, tamoxifen use in postmenopausal women with stage II+ or stage III breast cancer declined.Conclusions: The observed dissemination patterns suggest that the results of clinical trials are disseminated fairly rapidly to community-based physicians and their patients. [ABSTRACT FROM AUTHOR]

Evaluates the impact of reporting delay and error on cancer incidence rates and trends. Importance of reporting-adjusted cancer incidence rates in determining cancer incidence rates and trends; Measurement in the impact of cancer in the general population; Definition of reporting delay.

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: Area socioeconomic deprivation indices are widely used to monitor health disparities in Europe. However, such indices have not been used in cancer surveillance in the United States. We developed an area socioeconomic index to examine area socioeconomic patterns in all-cancer mortality among U.S. men between 1950 and 1998.Methods: Principal components analysis on 11 census variables was used to develop an area socioeconomic index that was then used to stratify all U.S. counties into one of five socioeconomic categories. The index was linked to 1950-1998 county mortality data to generate annual mortality rates for each area socioeconomic group. Joinpoint regression analysis was used to model mortality trends, and Poisson regression analysis was used to estimate socioeconomic gradients in mortality over time.Results: Area socioeconomic patterns in U.S. male cancer mortality changed dramatically between 1950 and 1998. Throughout the 1950s and 1960s, there was a positive socioeconomic gradient, with higher cancer mortality rates in high area socioeconomic groups than in low area socioeconomic groups. For example, in 1950-1952, cancer mortality was 49% (95% confidence interval [CI] = 41% to 59%) greater in the highest area socioeconomic group than in the lowest. The positive gradient narrowed in the 1970s, and by the late 1980s, socioeconomic differences in cancer mortality began to reverse and widen. In 1997-1998, cancer mortality was 19% (95% CI = 11% to 28%) higher in the lowest area socioeconomic group than in the highest. Gradients were steeper for men aged 25-64 years than for men aged 65 years or older.Conclusions: Socioeconomic patterns in male cancer mortality have reversed over time in the United States. Area socioeconomic indices could serve as a powerful surveillance tool for monitoring health disparities in cancer outcomes. [ABSTRACT FROM AUTHOR]

The potential confounding influence of changing treatment patterns and misattribution bias make a definitive conclusion about the link between PSA screening and mortality rates tentative at best. At least some of the mortality decline since 1991 appears likely to be due to screening, but the precise magnitude of the screening effect is difficult to estimate. The possible reduction in mortality due to screening, while uncertain, must be weighed against the substantial decrements in treatment-specific health outcomes (32-34) among men treated for clinically localized tumors typically detected by screening. Population data and ongoing screening trials in the United States and Europe (24, 35) will be complementary in the final determination of the relative contribution of the impact of screening versus other causes on recent mortality trends. [ABSTRACT FROM AUTHOR]

Background: The rise and fall of prostate cancer mortality correspond closely to the rise and fall of newly diagnosed cases. To understand this phenomenon, we explored the role that screening, treatment, iatrogenic (i.e., treatment-induced) deaths, and attribution bias (incorrect labeling of death from other causes as death from prostate cancer) have played in recent mortality trends.Methods: Join point regression is utilized to assess the recent rise and fall in mortality and the relationship of total U.S. trends to those areas served by the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Cancer Registry Program. Incidence-based mortality (IBM) is estimated with the use of prostate cancer data from the SEER Program to partition (from overall prostate cancer mortality trends) the contribution of cases diagnosed since the widespread use of prostate-specific antigen (PSA) testing starting in 1987. IBM is also used to examine the contribution of stage at diagnosis to the recent prostate cancer mortality trends.Results: IBM for cases diagnosed since 1987 rose above the pre-1987 secular (i.e., background) trend, peaked in the early 1990s, and almost returned to the secular trend by 1994. This rise and fall of IBM track with the pool of prevalent cases diagnosed within the prior 2 years. IBM for cases diagnosed with metastatic disease fell starting in 1991, while IBM for those diagnosed with localized/regional disease was relatively flat.Conclusions: The rise and fall in prostate cancer mortality observed since the introduction of PSA testing in the general population are consistent with a hypothesis that a fixed percent of the rising and falling pool of recently diagnosed patients who die of other causes may be mislabeled as dying of prostate cancer. The decline in IBM for distant stage disease and flat IBM trends for localized/regional disease provide some evidence of improved prognosis for screen-detected cases, although alternative interpretations are possible. [ABSTRACT FROM AUTHOR]

Background: The prostate-specific antigen test was approved by the U.S. Food and Drug Administration in 1986 to monitor the disease status in patients with prostate cancer and, in 1994, to aid in prostate cancer detection. However, after 1986, the test was performed on many men who had not been previously diagnosed with prostate cancer, apparently resulting in the diagnosis of a substantial number of early tumors. Our purpose is to provide insight into the effect of screening on prostate cancer rates. Detailed data are presented for whites because the size of the population allows for calculating statistically reliable rates; however, similar overall trends are seen for African-Americans and other races.Methods: Prostate cancer incidence data from the National Cancer Institute's Surveillance, Epidemiology, and End Results Program and mortality data from the National Center for Health Statistics were analyzed.Results/conclusions: The following findings are consistent with a screening effect: 1) the recent decrease since 1991 in the incidence of distant stage disease, after not having been perturbed by screening; 2) the decline in the incidence of earlier stage disease beginning the following year (i.e., 1992); 3) the recent increases and decreases in prostate cancer incidence and mortality by age that appear to indicate a calendar period effect; and 4) trends in the incidence of distant stage disease by tumor grade and trends in the survival of patients with distant stage disease by calendar year that provide suggestive evidence of the tendency of screening to detect slower growing tumors.Implications: The decline in the incidence of distant stage disease holds the promise that testing for prostate-specific antigen may lead to a sustained decline in prostate cancer mortality. However, population data are complex, and it is difficult to confidently attribute relatively small changes in mortality to any one cause. [ABSTRACT FROM AUTHOR]

The extensive pool of asymptomatic prostate disease in the population, which increases substantially with age, suggests that the frequent use of transurethral resection of the prostate (TURP) in recent decades has had a large effect on prostate cancer incidence. The authors identified the effect of TURP-detected prostate cancer on the observed incidence rates between 1973 and 1993 for men aged 65 years and older. They linked population-based cancer registry data from the Surveillance, Epidemiology, and End Results Program to Medicare records between 1986 and 1993 to determine whether a TURP occurred sufficiently close to the time of a prostate cancer diagnosis for them to assume that it led to the diagnosis. TURP-detected cases prior to 1986 were calculated using an indirect method that involved multiplying the TURP procedure rate in the general population (from the National Hospital Discharge Survey) by estimates of the proportion of TURPs resulting in a prostate cancer diagnosis (from Medicare data and the literature). TURP explained much of the observed increase in overall prostate cancer incidence between 1973 and 1986 and possibly all of it in men aged 70 years and older. However, its influence on the trend and overall magnitude of the rates diminished between 1987 and 1993. The changing role of TURP in detecting prostate cancer is attributed to changes in medical technology and screening practices. The declining influence of TURP on prostate cancer incidence is likely to have continued beyond the study period due to the recent introduction and increasing use of medications for treating obstructive uropathy. Am J Epidemiol 1999;150:848-60. [ABSTRACT FROM AUTHOR]

: 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. : 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. : 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. : 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. : 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. [J Natl Cancer Inst 85:892–897, 1993] [ABSTRACT FROM PUBLISHER]

Prostate cancer is known as a disease with an extremely high prevalence relative to its clinical incidence in the population. The combination of preclinical incidence and duration that could yield this phenomenon is of tremendous interest to researchers trying to understand the natural history of the disease and to develop efficient screening strategies. In this article, the authors present estimates of the age-specific asymptomatic incidence and average preclinical duration of prostate cancer. The methodological approach is to first estimate the age-specific incidence of new (stage Al) prostate cancers using preclinical prevalence data from autopsy studies performed between 1941 and 1964 and clinical incidence data for the years 1960–1986 from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute. Then, the preclinical prevalence estimates are divided by the derived preclinical incidence estimates to yield estimates of the average duration of asymptomatic disease. The estimated mean duration among white men is between 11 and 12 years and appears to be approximately 1 year shorter for blacks than for whites. Comparison of the lifetime risks of preclinical and clinical disease suggests that approximately 75% of prostate cancers will never become diagnosed if clinical incidence remains at levels observed in 1984–1986, prior to the introduction of prostate-specific antigen (PSA) screening in the population. Am J Epidemiol 1998; 148: 775–85. [ABSTRACT FROM AUTHOR]

High breast cancer mortality rates have been reported in the northeastern part of the United States, with recent attention focused on Long Island, New York. In this study, the authors investigate whether the high breast cancer mortality is evenly spread over the Northeast, in the sense that any observed clusters of deaths can be explained by chance alone, or whether there are clusters of statistical significance. Demographic data and age-specific breast cancer mortality rates for women were obtained for all 244 counties in 11 northeastern states and for the District of Columbia for 1988–1992. A recently developed spatial scan statistic is used, which searches for clusters of cases without specifying their size or location ahead of time, and which tests for their statistical significance while adjusting for the multiple testing inherent in such a procedure. The basic analysis is adjusted for age, with further analyses examining how the results are affected by incorporating race, urbanicity, and parity as confounding variables. There is a statistically significant and geographically broad cluster of breast cancer deaths in the New York City-Philadelphia, Pennsylvania, metropolitan area (p = 0.0001), which has a 7.4% higher mortality rate than the rest of the Northeast. The cluster remains significant when race, urbanicity, and/or parity are included as confounding variables. Four smaller subclusters within this area are also significant on their own strength: Philadelphia with suburbs (p = 0.0001), Long Island (p = 0.0001), central New Jersey (p = 0.0001), and northeastern New Jersey (p = 0.0001). The elevated breast cancer mortality on Long Island might be viewed less as a unique local phenomenon and more as part of a more general situation involving large parts of the New York City-Philadelphia metropolitan area. The several known and hypothesized risk factors for which we could not adjust and that may explain the detected cluster are most notably age at first birth, age at menarche, age at menopause, breastfeeding, genetic mutations, and environmental factors. Am J Epidemiol 1997;146:161-70. [ABSTRACT FROM AUTHOR]