Cancer statistics, 2014.

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data were collected by the National Cancer Institute, the Centers for Disease Control and Prevention, and the North American Association of Central Cancer Registries and mortality data were collected by the National Center for Health Statistics. A total of 1,665,540 new cancer cases and 585,720 cancer deaths are projected to occur in the United States in 2014. During the most recent 5 years for which there are data (2006-2010), delay-adjusted cancer incidence rates declined slightly in men (by 0.6% per year) and were stable in women, while cancer death rates decreased by 1.8% per year in men and by 1.4% per year in women. The combined cancer death rate (deaths per 100,000 population) has been continuously declining for 2 decades, from a peak of 215.1 in 1991 to 171.8 in 2010. This 20% decline translates to the avoidance of approximately 1,340,400 cancer deaths (952,700 among men and 387,700 among women) during this time period. The magnitude of the decline in cancer death rates from 1991 to 2010 varies substantially by age, race, and sex, ranging from no decline among white women aged 80 years and older to a 55% decline among black men aged 40 years to 49 years. Notably, black men experienced the largest drop within every 10-year age group. Further progress can be accelerated by applying existing cancer control knowledge across all segments of the population.

Effect of using diode laser on Enterococcus faecalis and its lipoteichoic acid (LTA) in chronic apical periodontitis.

The purpose of this study was to evaluate the effect of diode laser irradiation on Enterococcus faecalis (E. faecalis) and its lipoteichoic acid (LTA). Ninety-six freshly extracted single-rooted teeth were divided into six groups, n = 8 per group. Groups 1, 2, 3, and 4 as laser group (810 nm PILOT™ Diode Laser, 400 µm fiber diameter, continuous mode, 30 s time) with powers at 1.0 W, 1.5 W, 2.0 W, and 2.5 W respectively. Group 5 or positive control group (3 ml of 1% sodium hypochlorite (NaOCl) irrigation) and group 6 or negative control group (3 ml of normal saline (0.9% NaCl) irrigation). Root canal samples were collected before and after receiving laser irradiation and irrigation solution. Cultivable bacteria were determined by counting the colony (CFU/ml). Evaluation of temperature on the external root surface of teeth was done with K type thermocouple using laser at different powers. Enzyme-linked immunosorbant assay (ELISA) was performed to measure the LTA levels and the correlations between E. faecalis count, LTA levels, and rise in temperature were observed using Pearson's correlation test. E. faecalis LTA was subjected to laser irradiation and its structural damage was examined by thin layer chromatography (TLC). Compared with the control groups, all laser groups showed a decreased colony counts and decreased LTA levels with statistically significant difference (p Ë‚ 0.05). The bactericidal effect and LTA reduction of laser was better at 2.5 W power. Laser at 2.5 W power had temperature rise of more than 7 °C which is beyond the safe thermal threshold level. No statistically significant correlation was found between E. faecalis count, levels of LTA, and rise in external root surface temperature (p Ëƒ 0.05). TLC results showed a structural damage in the glycolipid moiety of E. faecalis LTA. Diode laser can effectively reduce the E. faecalis count and its LTA levels.

Statistical inferences of extended concentration indices for directly standardized rates.

The relative concentration index (RCI) and the absolute concentration index (ACI) have been widely used for monitoring health disparities with ranked health determinants. The RCI has been extended to allow value judgments about inequality aversion by Pereira in 1998 and by Wagstaff in 2002. Previous studies of the extended RCI have focused on survey sample data. This paper adapts the extended RCI for use with directly standardized rates (DSRs) calculated from population-based surveillance data. A Taylor series linearization (TL)-based variance estimator is developed and evaluated using simulations. A simulation-based Monte Carlo (MC) variance estimator is also evaluated as a comparison. Following Wagstaff's approach in 1991, we extend the ACI for use with DSRs. In all simulations, both the TL and MC methods produce valid variance estimates. The TL variance estimator has a simple, closed form that is attractive to users without sophisticated programming skills. The TL and MC estimators have been incorporated into a beta version of the National Cancer Institute's Health Disparities Calculator, a free statistical software tool that enables the estimation of 11 commonly used summary measures of health disparities for DSRs.

Analyzing discrete competing risks data with partially overlapping or independent data sources and nonstandard sampling schemes, with application to cancer registries.

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

Early estimates of cancer incidence for 2015: Expanding to include estimates for white and black races.

BACKGROUND: The National Cancer Institute's cancer incidence estimates through 2015 from the Surveillance, Epidemiology, and End Results (SEER) registries' November 2017 submission are released in April 2018. METHODS: Early estimates (February 2017) of cancer incidence rates and trends from the SEER 18 registries for diagnoses in 2000 through 2015 were evaluated with a revised delay-adjustment model, which was used to adjust for the undercount of cases in the early release. For the first time, early estimates were produced for race (whites and blacks) along with estimates for new sites: the oral cavity and pharynx, leukemia, and myeloma. RESULTS: Model validation comparing delay-adjusted rates and trends through 2014 and using 2016 submissions showed good agreement. Differences in trends through 2015 in comparison with those through 2014 were evident. The rate of female breast cancer rose significantly from 2004 to 2015 by 0.3% per year (annual percent change [APC] = 0.3%); the prior trend through 2014 (the same magnitude) was not yet significant. The female colon and rectum cancer trend for whites became flat after previously declining. Lung and bronchus cancer for whites showed a significant decline (APC for males = -2.3%, 2012-2015; APC for females = -0.7%, 2011-2015). Thyroid cancer for black females changed from a continuous rise to a flat final segment (APC = 1.6%, not significant, 2011-2015). Both kidney and renal pelvis cancer (APC = 1.5%, 2011-2015) and childhood cancers (APC = 0.5%, 2000-2015) for white males showed a significant rise in the final segments from previously flat trends. Kidney and renal pelvis cancer for black males showed a change from a significant rise to a flat trend. CONCLUSIONS: The early release of SEER data continues to be useful as a preliminary estimate of the most current cancer incidence trends. Cancer 2018;124:2192-204. © 2018 American Cancer Society.

Early estimates of SEER cancer incidence, 2014.

BACKGROUND: Cancer incidence rates and trends for cases diagnosed through 2014 using data reported to the Surveillance, Epidemiology, and End Results (SEER) program in February 2016 and a validation of rates and trends for cases diagnosed through 2013 and submitted in February 2015 using the November 2015 submission are reported. New cancer sites include the pancreas, kidney and renal pelvis, corpus and uterus, and childhood cancer sites for ages birth to 19 years inclusive. METHODS: A new reporting delay model is presented for these estimates for more consistent results with the model used for the usual November SEER submissions, adjusting for the large case undercount in the February submission. Joinpoint regression methodology was used to assess trends. Delay-adjusted rates and trends were checked for validity between the February 2016 and November 2016 submissions. RESULTS: Validation revealed that the delay model provides similar estimates of eventual counts using either February or November submission data. Trends declined through 2014 for prostate and colon and rectum cancer for males and females, male and female lung cancer, and cervical cancer. Thyroid cancer and liver and intrahepatic bile duct cancer increased. Pancreas (male and female) and corpus and uterus cancer demonstrated a modest increase. Slight increases occurred for male kidney and renal pelvis, and for all childhood cancer sites for ages birth to 19 years. CONCLUSIONS: Evaluating early cancer data submissions, adjusted for reporting delay, produces timely and valid incidence rates and trends. The results of the current study support using delay-adjusted February submission data for valid incidence rate and trend estimates over several data cycles. Cancer 2017;123:2524-34. © 2017 American Cancer Society.

Adjusting for reporting delay in cancer incidence when combining different sets of cancer registries.

Cancer registries collect cancer incidence data that can be used to calculate incidence rates in a population and track changes over time. For incidence rates to be accurate, it is critical that diagnosed cases be reported in a timely manner. Registries typically allow a fixed amount of time (e.g. two years) for diagnosed cases to be reported before releasing the initial case counts for a particular diagnosis year. Inevitably, however, additional cases are reported after the initial counts are released; these extra cases are included in subsequent releases that become more complete over time, while incidence rates based on earlier releases will underestimate the true rates. Statistical methods have been developed to estimate the distribution of reporting delay (the amount of time until a diagnosed case is reported) and to correct incidence rates for underestimation due to reporting delay. Since the observed reporting delays must be less than the length of time the registry has been collecting data, most methods estimate a truncated delay distribution. These methods can be applied to a group of registries that began collecting data in the same diagnosis year. In this paper, we extend the methods to two groups of registries that began collecting data in two different diagnosis years (so that the delay distributions are truncated at different times). We apply the proposed method to data from the National Cancer Institute's Surveillance Epidemiology and End Results (SEER) program, a consortium of U.S. cancer registries that includes nine registries with data collection beginning in 1981 and four registries with data collection beginning in 1992. We use the method to obtain delay-adjusted incidence rates for melanoma, liver cancer, and Hodgkin lymphoma.

Prediction of Risk of Metastatic Recurrence for Female Breast Cancer Patients in the Presence of Competing Causes of Death.

BACKGROUND: To estimate risk of recurrence for women diagnosed with nonmetastatic breast cancer considering the risks of other causes mortality. METHODS: We extend a method based on the diagnosis-metastasis-death pathway to include risks of other causes mortality. We estimate three probabilities as cumulative incidence of: (i) being alive and recurrence-free, (ii) death for other causes before a recurrence, and (iii) recurrence. We apply the method to female breast cancer relative survival from the Surveillance, Epidemiology, and End Results Program registries (2000-2018) data. RESULTS: The cumulative incidence of recurrence shows a higher increase with more advanced cancer stage and is less influenced by age at diagnosis. At 5 years from diagnosis, the cumulative incidence of recurrence is less than 3% for those diagnosed with stage I, 10% to 13% for those diagnosed with stage II, and 37% to 47% for those diagnosed with stage III breast cancer. The estimates of recurrence considering versus ignoring the risks of dying from other causes were generally consistent, except for older women with more advanced stage, and longer time since diagnosis. In these groups, the net probability of recurrence, excluding the risks of dying from other causes, were overestimated. CONCLUSIONS: For patients with cancer who are older or long-term survivors, it is important to include the risks of other cause mortality as the crude cumulative incidence of recurrence is a more appropriate measure. IMPACT: These estimates are important in clinical decision making, as higher competing mortality may preclude the benefits of aggressive treatments.

Key Points for Clinicians About the SEER Oral Cancer Survival Calculator.

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.

A New Personalized Oral Cancer Survival Calculator to Estimate Risk of Death From Both Oral Cancer and Other Causes.

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.

Predicting US- and state-level cancer counts for the current calendar year: Part II: evaluation of spatiotemporal projection methods for incidence.

BACKGROUND: The current study was undertaken to evaluate the spatiotemporal projection models applied by the American Cancer Society to predict the number of new cancer cases. METHODS: Adaptations of a model that has been used since 2007 were evaluated. Modeling is conducted in 3 steps. In step I, ecologic predictors of spatiotemporal variation are used to estimate age-specific incidence counts for every county in the country, providing an estimate even in those areas that are missing data for specific years. Step II adjusts the step I estimates for reporting delays. In step III, the delay-adjusted predictions are projected 4 years ahead to the current calendar year. Adaptations of the original model include updating covariates and evaluating alternative projection methods. Residual analysis and evaluation of 5 temporal projection methods were conducted. RESULTS: The differences between the spatiotemporal model-estimated case counts and the observed case counts for 2007 were < 1%. After delays in reporting of cases were considered, the difference was 2.5% for women and 3.3% for men. Residual analysis indicated no significant pattern that suggested the need for additional covariates. The vector autoregressive model was identified as the best temporal projection method. CONCLUSIONS: The current spatiotemporal prediction model is adequate to provide reasonable estimates of case counts. To project the estimated case counts ahead 4 years, the vector autoregressive model is recommended to be the best temporal projection method for producing estimates closest to the observed case counts.

The Cancer Survival Query System: making survival estimates from the Surveillance, Epidemiology, and End Results program more timely and relevant for recently diagnosed patients.

BACKGROUND: Population-based cancer registries that include patient follow-up generally provide information regarding net survival (ie, survival associated with the risk of dying of cancer in the absence of competing risks). However, registry data also can be used to calculate survival from cancer in the presence of competing risks, which is more clinically relevant. METHODS: Statistical methods were developed to predict the risk of death from cancer and other causes, as well as natural life expectancy if the patient did not have cancer based on a profile of prognostic factors including characteristics of the cancer, demographic factors, and comorbid conditions. The Surveillance, Epidemiology, and End Results (SEER) Program database was used to calculate the risk of dying of cancer. Because the risks of dying of cancer versus other causes are assumed to be independent conditional on the prognostic factors, a wide variety of independent data sources can be used to calculate the risk of death from other causes. Herein, the risk of death from other causes was estimated using SEER and Medicare claims data, and was matched to the closest fitting portion of the US life table to obtain a "health status-adjusted age." RESULTS: A nomogram was developed for prostate cancer as part of a Web-based Cancer Survival Query System that is targeted for use by physicians and patients to obtain information on a patient's prognosis. More nomograms currently are being developed. CONCLUSIONS: Nomograms of this type can be used as one tool to assist cancer physicians and their patients to better understand their prognosis and to weigh alternative treatment and palliative strategies.

Protective effect of low-level laser irradiation on lipopolysaccharide-mediated inflammatory injury of human periodontal ligament fibroblasts.

OBJECTIVES: To study the effect and mechanism of low-level laser irradiation (LLLI) on lipopolysaccharide (LPS)-induced inflammatory injury of human periodontal ligament fibroblasts (hPDLFs). METHODS: hPDLFs were inoculated into well plates and randomly divided into the normal group, LPS group, and LPS+LLLI group. The cells in the normal group were cultured in conventional medium. The hPDLFs in the LPS and LPS+LLLI groups were cultured in RPMI1640 medium containing 1 mg·L-1 LPS. The three subgroups of the LPS+LLLI group were exposed to different LLLI. After 4 days, the cell apoptosis, viability, and intracellular free Ca2+ concentration of each group were measured. The contents of tumor necrosis factor-α (TNF-α), interleukin (IL)-8, IL-1ß, and IL-6 were measured by enzyme linked immunosorbent assay (ELISA). Reverse transcription-polymerase chain reaction (RT-PCR) and Western blot were used to detect the expression of matrix metalloproteinase (MMP)-2, MMP-3, and MMP-9 genes and proteins of hPDLFs in each group. RESULTS: Compared with the normal group, the LPS group showed increased apoptosis rate of hPDLFs and intracellular free Ca2+concentration and decreased cell viability (P<0.05). The TNF-α, IL-8, IL-1ß, and IL-6 levels were higher in the cell supernatant (P<0.05), and the expression of MMP-2, MMP-3, and MMP-9 genes and proteins of hPDLFs was significantly increased (P<0.05). Compared with the LPS group, the LPS+LLLI group showed significantly decreased apoptosis rate and intracellular free Ca2+ concentration and significantly increased cell viability (P<0.05). The TNF-α, IL-8, IL-1ß, and IL-6 levels in the supernatant of cells and the expression of MMP-2, MMP-3, and MMP-9 genes and proteins of hPDLFs were significantly decreased (P<0.05). CONCLUSIONS: LLLI has a protective effect on the inflammatory injury of hPDLFs induced by LPS, and the effect is most obvious when the irradiation intensity is 4 J·cm-2.

Covariate adjusted weighted normal spatial scan statistics with applications to study geographic clustering of obesity and lung cancer mortality in the United States.

In the field of cluster detection, a weighted normal model-based scan statistic was recently developed to analyze regional continuous data and to evaluate the clustering pattern of pre-defined cells (such as state, county, tract, school, hospital) that include many individuals. The continuous measures of interest are, for example, the survival rate, mortality rate, length of physical activity, or the obesity measure, namely, body mass index, at the cell level with an uncertainty measure for each cell. In this paper, we extend the method to search for clusters of the cells after adjusting for single/multiple categorical/continuous covariates. We apply the proposed method to 1999-2003 obesity data in the United States (US) collected by CDC's Behavioral Risk Factor Surveillance System with adjustment for age and race, and to 1999-2003 lung cancer age-adjusted mortality data by gender in the United States from the Surveillance Epidemiology and End Results (SEER Program) with adjustment for smoking and income.

Exposure to diode laser (810nm) affects the bacterial adherence and biofilm formation in a E. faecalis biofilm model.

BACKGROUND: A large number of studies have shown that diode laser can effectively kill Enterococcus faecalis (E. faecalis). However, to our knowledge there has been little information regarding high-level analysis of sterilization mechanism on E. faecalis biofilm models after laser irradiation. This study provides emphasis on the E. faecalis viability and exopolysaccharide content after laser irradiation. It also aims to examine whether diode laser affects the biofilm formation and adherence of E. faecalis biofilm at the level of gene expression. METHODS: E. faecalis (ATCC 29,212) was inoculated for biofilm formation. After growing for 48 h, the biofilms were submitted to the following treatments, twice daily (n = 6): (i) Diode laser with energy densities as 28.7 J/cm2; (ii) 1% sodium hypochlorite (NaOCl) was used as a positive control; (iii) 0.9 % physiological saline (NaCl) was used as a negative control. After 120 h of biofilm growth, bactericidal activity of diode laser was evaluated by using plate count method and scanning electron microscopy (SEM). The distribution of extracellular polysaccharide was assessed by Confocal laser scanning microscopy (CLSM). Anthrone method was used to quantify the amount of water-soluble exopolysaccharide (WSE) and water-insoluble exopolysaccharides (WIE) in E. faecalis. Real-time quantitative polymerase chain reaction (RT-qPCR) was employed to assess transcription of genes (gelE, ace and esp) related to formation and adherence of E. faecalis biofilm. RESULTS: Compared with NaCl, diode laser significantly destabilizedE. faecalis biofilm, which showing a decrease in the number of bacteria and inhibiting the adherence of biofilm (p < 0.05). From Anthrone method and CLSM, the study found that diode laser lowered the concentration of WSE and WIE in biofilms (p < 0.05). In addition, transcription of gene gelE, ace and esp were also reduced after irradiation of diode laser (p < 0.05). CONCLUSION: By inhibiting the synthesis of WIE and WSE, diode laser may reduce the formation of E. faecalis biofilm. The expression of specific genes (gelE, ace and esp) involved in bacterial adherence and biofilm formation were down regulated under the exposure to diode laser (810 nm), which in turn is expected to decrease the pathogenicity of E. faecalis.

An age-stratified poisson model for comparing trends in cancer rates across overlapping regions.

The annual percent change (APC) has been used as a measure to describe the trend in the age-adjusted cancer incidence or mortality rate over relatively short time intervals. The yearly data on these age-adjusted rates are available from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The traditional methods to estimate the APC is to fit a linear regression of logarithm of age-adjusted rates on time using the least squares method or the weighted least squares method, and use the estimate of the slope parameter to define the APC as the percent change in the rates between two consecutive years. For comparing the APC for two regions, one uses a t-test which assumes that the two datasets on the logarithm of the age-adjusted rates are independent and normally distributed with a common variance. Two modifications of this test, when there is an overlap between the two regions or between the time intervals for the two datasets have been recently developed. The first modification relaxes the assumption of the independence of the two datasets but still assumes the common variance. The second modification relaxes the assumption of the common variance also, but assumes that the variances of the age-adjusted rates are obtained using Poisson distributions for the mortality or incidence counts. In this paper, a unified approach to the problem of estimating the APC is undertaken by modeling the counts to follow an age-stratified Poisson regression model, and by deriving a corrected Z -test for testing the equality of two APCs. A simulation study is carried out to assess the performance of the test and an application of the test to compare the trends, for a selected number of cancer sites, for two overlapping regions and with varied degree of overlapping time intervals is presented.

Can We Use Survival Data from Cancer Registries to Learn about Disease Recurrence? The Case of Breast Cancer.

Background: Population-representative risks of metastatic recurrence are not generally available because cancer registries do not collect data on recurrence. This article presents a novel method that estimates the risk of recurrence using cancer registry disease-specific survival.Methods: The method is based on an illness-death process coupled with a mixture cure model for net cancer survival. The risk of recurrence is inferred from the estimated survival among the noncured fraction and published data on survival after recurrence. We apply the method to disease-specific survival curves from female breast cancer cases without a prior cancer diagnosis and with complete stage and hormone receptor (HR) status in Surveillance, Epidemiology and End Results registries (1992-2013).Results: The risk of recurrence is higher for women diagnosed with breast cancer at older age, earlier period, more advanced stage, and HR-negative tumors. For women diagnosed at ages 60-74 in 2000-2013, the projected percent recurring within 5 years is 2.5%, 9.6%, and 34.5% for stages I, II, and III HR-positive, and 6.5%, 20.2%, and 48.5% for stages I, II, and III HR-negative tumors. Although HR-positive cases have lower risk of recurrence soon after diagnosis, their risk persists longer than for HR-negative cases. Results show a high degree of robustness to model assumptions.Conclusions: The results show that it is possible to extract information about the risk of recurrence using disease-specific survival, and the methods can in principle be extended to other cancer sites.Impact: This study provides the first population-based summaries of the risk of breast cancer recurrence in U.S. women. Cancer Epidemiol Biomarkers Prev; 27(11); 1332-41. ©2018 AACR.

Geographical, racial and socio-economic variation in life expectancy in the US and their impact on cancer relative survival.

PURPOSE: Despite gains in life expectancy between 1992 to 2012, large disparities in life expectancy continue to exist in the United States between subgroups of the population. This study aimed to develop detailed life tables (LT), accounting for mortality differences by race, geography, and socio-economic status (SES), to more accurately measure relative cancer survival and life expectancy patterns in the United States. METHODS: We estimated an extensive set of County SES-LT by fitting Poisson regression models to deaths and population counts for U.S. counties by age, year, gender, race, ethnicity and county-level SES index. We reported life expectancy patterns and evaluated the impact of the County SES-LT on relative survival using data from the Surveillance Epidemiology and End Results (SEER) Program cancer registries. RESULTS: Between 1992 and 2012, the largest increase in life expectancy was among black men (6.8 years), however there were still large geographical differences. Life expectancy was highest for Asian or Pacific Islanders (API), and lowest for American Indians and Alaskan Natives (AIAN). In 2010, life expectancies by state ranged from 73 to 82 years for white males, 78 to 86 years for white females, 66 to 75 for black males, and 75 to 81 for black females. Comparisons of relative survival using National LT and the new County SES-LT showed that relative survival using County SES-LT improved relative survival estimates for some demographic groups, particularly in low and high SES areas, among Hispanics and AIAN, and among older male cancer patients. Relative survival using County SES-LT was 7.3% and 6.7% survival points closer to cause-specific survival compared to the National LT relative survival for AIAN and Hispanic cancer patients diagnosed between ages 75 and 84 years, respectively. Importantly, the County SES-LT relative survival estimates were higher in lower SES areas and lower in higher SES areas, reducing differences in relative survival comparisons. CONCLUSION: The use of these new socio-economic life tables (County SES-LT) can provide more accurate estimates of relative survival, improve comparisons of relative survival among registries, better illustrate disparities and cancer control efforts, and should be used as default for cancer relative survival using U.S. data.

Efficient interval estimation for age-adjusted cancer rates.

The age-adjusted cancer rates are defined as the weighted average of the age-specific cancer rates, where the weights are positive, known, and normalized so that their sum is 1. Fay and Feuer developed a confidence interval for a single age-adjusted rate based on the gamma approximation. Fay used the gamma approximations to construct an F interval for the ratio of two age-adjusted rates. Modifications of the gamma and F intervals are proposed and a simulation study is carried out to show that these modified gamma and modified F intervals are more efficient than the gamma and F intervals, respectively, in the sense that the proposed intervals have empirical coverage probabilities less than or equal to their counterparts, and that they also retain the nominal level. The normal and beta confidence intervals for a single age-adjusted rate are also provided, but they are shown to be slightly liberal. Finally, for comparing two correlated age-adjusted rates, the confidence intervals for the difference and for the ratio of the two age-adjusted rates are derived incorporating the correlation between the two rates. The proposed gamma and F intervals and the normal intervals for the correlated age-adjusted rates are recommended to be implemented in the Surveillance, Epidemiology and End Results Program of the National Cancer Institute.

Prioritizing US Cervical Cancer Prevention With Results From a Geospatial Model.

PURPOSE: To determine if differences in screening and vaccination patterns across the population may accentuate ethnic and geographic variation in future burden of disease. METHODS: Using Cancer in North America data provided by the North American Association of Central Cancer Registries, county cervical cancer incidence trends from 1995 to 2009 were modeled for the entire United States using ecologic covariates. Rates for health service areas were also modeled by ethnicity. State-level incidence was mapped together with Papanicolaou (Pap) screening, past 3 years (women ≥ 18 years old), and three-dose human papillomavirus (HPV) vaccine coverage (girls 13 to 17 years old) to identify potential priority areas for preventive services. RESULTS: US cervical cancer incidence decreased more during the periods 1995 to 1999 and 2000 to 2004 than during the period 2005 to 2009. During these 15 years, the most affected areas became increasingly confined to Appalachia, the lower Mississippi Valley, the Deep South, Texas, and Florida. Hispanic and black women experienced a higher incidence of cervical cancer than both white and Asian and Pacific Islander women during each period. Women in 10 of 17 states/districts with a high incidence (≥ 8.14/100,000) reported low Pap testing (< 78.5%), HPV vaccine coverage (< 33.9%), or both prevention technologies. CONCLUSION: The decline in cervical cancer incidence has slowed in recent years. Access to HPV vaccination, targeted screening, and treatment in affected populations is needed to reduce cervical cancer disparities in the future.