CP*Trends: An Online Tool for Comparing Cohort and Period Trends Across Cancer Sites.

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.

Head and neck squamous cell cancers in the United States are rare and the risk now is higher among white individuals compared with black individuals.

BACKGROUND: The increasing incidence of oropharyngeal squamous cell cancer (OPSCC) is well established. However, up-to-date incidence estimates and trends for head and neck squamous cell cancers (HNSCCs) overall, including major anatomic sites, and nonoropharyngeal (non-OP) HNSCCs by sex, race, and age in the United States are not well described. METHODS: A retrospective analysis of incident HNSCCs during 1992 through 2014 using the Surveillance, Epidemiology, and End Results database was performed to evaluate the incidence of HNSCCs overall, OPSCC, and non-OP HNSCC (those of the larynx, oral cavity, hypopharynx, nasopharynx, and nasal cavity). Incidence rates were calculated overall and by subgroups of interest, and incidence rate ratios were used to compare rates between groups. The incidence rates presented were per 100,000 population and were age adjusted to the 2000 US standard population (19 age groups; Census P25-1130). The annual percent change (APC) was modeled with and without joinpoints. RESULTS: The incidence of HNSCC overall declined (average APC [aAPC], -0.8; P<.001) despite significant increases in the incidence of OPSCCs, most notably between 2000 and 2014 (APC, 2.1; P<.001). Significant declines in incidence were observed for all non-OP HNSCC sites for both women and men (P<.001 each). Among women, the risk of OPSCC also significantly decreased (aAPC, -0.8; P = .002), whereas the risk among men was stable during 1992 through 2001 (APC, 0.4; P = .42) and then significantly increased from 2001 to 2014 (APC, 2.7; P<.001). Decreases in the risk of non-OP HNSCC were especially large for black women (aAPC, -2.6; P<.001) and men (aAPC, -3.0; P<.001). Although the incidence of HNSCC previously was highest among black individuals, since 2009 its incidence has been higher among white compared with black individuals. CONCLUSIONS: The incidence of HNSCC is declining, especially for non-OP HNSCC and among black individuals. Cancer 2018;124:2125-33. © 2018 American Cancer Society.

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.

Collaborative Modeling of the Benefits and Harms Associated With Different U.S. Breast Cancer Screening Strategies.

BACKGROUND: Controversy persists about optimal mammography screening strategies. OBJECTIVE: To evaluate screening outcomes, taking into account advances in mammography and treatment of breast cancer. DESIGN: Collaboration of 6 simulation models using national data on incidence, digital mammography performance, treatment effects, and other-cause mortality. SETTING: United States. PATIENTS: Average-risk U.S. female population and subgroups with varying risk, breast density, or comorbidity. INTERVENTION: Eight strategies differing by age at which screening starts (40, 45, or 50 years) and screening interval (annual, biennial, and hybrid [annual for women in their 40s and biennial thereafter]). All strategies assumed 100% adherence and stopped at age 74 years. MEASUREMENTS: Benefits (breast cancer-specific mortality reduction, breast cancer deaths averted, life-years, and quality-adjusted life-years); number of mammograms used; harms (false-positive results, benign biopsies, and overdiagnosis); and ratios of harms (or use) and benefits (efficiency) per 1000 screens. RESULTS: Biennial strategies were consistently the most efficient for average-risk women. Biennial screening from age 50 to 74 years avoided a median of 7 breast cancer deaths versus no screening; annual screening from age 40 to 74 years avoided an additional 3 deaths, but yielded 1988 more false-positive results and 11 more overdiagnoses per 1000 women screened. Annual screening from age 50 to 74 years was inefficient (similar benefits, but more harms than other strategies). For groups with a 2- to 4-fold increased risk, annual screening from age 40 years had similar harms and benefits as screening average-risk women biennially from 50 to 74 years. For groups with moderate or severe comorbidity, screening could stop at age 66 to 68 years. LIMITATION: Other imaging technologies, polygenic risk, and nonadherence were not considered. CONCLUSION: Biennial screening for breast cancer is efficient for average-risk populations. Decisions about starting ages and intervals will depend on population characteristics and the decision makers' weight given to the harms and benefits of screening. PRIMARY FUNDING SOURCE: National Institutes of Health.

Preliminary estimates of SEER cancer incidence for 2013.

BACKGROUND: This article presents a first look at rates and trends for cases in the Surveillance, Epidemiology, and End Results (SEER) program diagnosed through 2013 using the February 2015 submission, and a validation of rates and trends from the February 2014 submission using the subsequent November 2014 submission. To the authors' knowledge, this is the second time SEER has published trends based on the early February submission. Three new cancer sites were added: cervix, thyroid, and liver/ intrahepatic bile duct. METHODS: A reporting delay model adjusted for the undercount of cases, which is substantially larger for the February than the subsequent November submission, was used. Joinpoint regression methodology was used to assess trends. Delay-adjusted rates and trends were checked to assess validity between the February and November 2014 submissions. RESULTS: The validation of rates and trends from the February and November 2014 submissions demonstrated even better agreement than the previously reported comparison between the February and November 2013 submissions, thereby affording additional confidence that the delay-adjusted February submission data can be used to produce valid estimates of incidence trends. Trends for cases diagnosed through 2013 revealed more rapid declines in female colon and rectal cancer and prostate cancer. A plateau in female melanoma trends and a slowing of the increases in thyroid cancer and male liver/intrahepatic bile duct cancer trends were observed. CONCLUSIONS: Analysis of early cancer data submissions can provide a preliminary indication of differences in incidence trends with an additional year of data. Although the delay adjustment correction adjusts for underreporting of cases, caution should be exercised when interpreting the results in this early submission. Cancer 2016;122:1579-87. © 2016 American Cancer Society.

Early estimates of SEER cancer incidence for 2012: Approaches, opportunities, and cautions for obtaining preliminary estimates of cancer incidence.

BACKGROUND: The National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program collects and publishes population-based cancer incidence data from registries covering approximately 28% (seer.cancer.gov/registries/data.html) of the US population. SEER incidence rates are released annually in April from data submitted the prior November. The time needed to identify, consolidate, clean, and submit data requires the latest diagnosis year included to be 3 years before release. Approaches, opportunities, and cautions for an earlier release of data based on a February submission are described. METHODS: First, cases submitted in February for the latest diagnosis year represented 92% to 98% of those in the following November submission. A reporting delay model was used to statistically adjust counts in recent diagnosis years for cases projected in the future. February submissions required larger adjustment factors than November submissions. Second, trends were checked to assess the validity. RESULTS: Most cancer sites had similar annual percent change (APC) trends for February and November 2013. Male colon and rectum cancer and female lung and bronchus cancer showed an acceleration in declining APC trends only in February. Average annual percent change (AAPC) trends for the 2 submissions were similar for all sites. CONCLUSIONS: For the first time, preliminary 2012 incidence rates, based on February submissions, are provided. An accelerated decline starting in 2008 for male colon and rectum cancer rates and male lung cancer rates did not persist when 2012 data were added. An earlier release of SEER data is possible. Caution must be exercised when one is interpreting changing trends. Use of the more conservative AAPC is advised.

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.

Predicting US- and state-level cancer counts for the current calendar year: Part I: evaluation of temporal projection methods for mortality.

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

Chapter 5: Actual and counterfactual smoking prevalence rates in the U.S. population via microsimulation.

The smoking history generator (SHG) developed by the National Cancer Institute simulates individual life/smoking histories that serve as inputs for the Cancer Intervention and Surveillance Modeling Network (CISNET) lung cancer models. In this chapter, we review the SHG inputs, describe its outputs, and outline the methodology behind it. As an example, we use the SHG to simulate individual life histories for individuals born between 1890 and 1984 for each of the CISNET smoking scenarios and use those simulated histories to compute the corresponding smoking prevalence over the period 1975-2000.

Improved population-based probability of developing cancer when direct estimates of the cancer-free population are available.

Age-conditional probabilities of developing a first cancer represent the transition from being cancer-free to developing a first cancer. Natural inputs into their calculation are rates of first cancer per person-years alive and cancer-free. However these rates are not readily available because they require information on the cancer-free population. Instead rates of first cancer per person-years alive, calculated using as denominator the mid-year populations, available from census data, can be easily calculated from cancer registry data. Methods have been developed to estimate age-conditional probabilities of developing cancer based on these easily available rates per person-years alive that do not directly account for the cancer-free population. In the last few years models (Merrill et al., Int J Epidemiol 29(2):197-207, 2000; Mariotto et al., SEER Cancer Statistics Review, 2002; Clegg et al., Biometrics 58(3):684-688, 2002; Gigli et al., Stat Methods Med Res 15(3):235-253, 2006, and software (ComPrev:Complete Prevalence Software, Version 1.0, 2005) have been developed that allow estimation of cancer prevalence (DevCan: Probability of Developing or Dying of Cancer Software, Version 6.0, 2005). Estimates of population-based cancer prevalence allows for the estimation of the cancer-free population and consequently of rates per person-years alive and cancer-free. In this paper we present a method that directly estimates the age-conditional probabilities of developing a first cancer using rates per person-years alive and cancer-free obtained from prevalence estimates. We explore conditions when the previous and the new estimators give similar or different values using real data from the Surveillance, Epidemiology and End Results (SEER) program.

Bias associated with self-report of prior screening mammography.

BACKGROUND: Self-reported screening behaviors from national surveys often overestimate screening use, and the amount of overestimation may vary by demographic characteristics. We examine self-report bias in mammography screening rates overall, by age, and by race/ethnicity. METHODS: We use mammography registry data (1999-2000) from the Breast Cancer Surveillance Consortium to estimate the validity of self-reported mammography screening collected by two national surveys. First, we compare mammography use from 1999 to 2000 for a geographically defined population (Vermont) with self-reported rates in the prior two years from the 2000 Vermont Behavioral Risk Factor Surveillance System. We then use a screening dissemination simulation model to assess estimates of mammography screening from the 2000 National Health Interview Survey. RESULTS: Self-report estimates of mammography use in the prior 2 years from the Vermont Behavioral Risk Factor Surveillance System are 15 to 25 percentage points higher than actual screening rates across age groups. The differences in National Health Interview Survey screening estimates from models are similar for women 40 to 49 and 50 to 59 years and greater than for those 60 to 69, or 70 to 79 (27 and 26 percentage points versus 14, and 14, respectively). Overreporting is highest among African American women (24.4 percentage points) and lowest among Hispanic women (17.9) with non-Hispanic White women in between (19.3). Values of sensitivity and specificity consistent with our results are similar to previous validation studies of mammography. CONCLUSION: Overestimation of self-reported mammography usage from national surveys varies by age and race/ethnicity. A more nuanced approach that accounts for demographic differences is needed when adjusting for overestimation or assessing disparities between populations.

Reconstructing PSA testing patterns between black and white men in the US from Medicare claims and the National Health Interview Survey.

BACKGROUND: Frequencies of prostate-specific antigen (PSA) test administration were not actively monitored on a national level during the first decade of PSA testing. The objectives of this article were to reconstruct patterns of PSA testing between black and white men in the US and to determine the extent of any racial disparity in PSA use. METHODS: Data from the 2000 National Health Interview Survey were used to model the adoption of PSA and to estimate the distribution of age at first test. Longitudinal Medicare claims data were used to estimate the distribution of intervals between tests. The rates of initial and subsequent tests were then combined by simulation to reconstruct individual screening histories. Results are from the reconstructed model. RESULTS: Overall, 45% of white men and 43% of black men within ages 40-84 years had at least 1 PSA test by the year 2000. The authors found that among older men, whites adopted PSA screening earlier than blacks, whereas among younger men, this trend was reversed, with blacks adopting screening earlier than whites. Annual testing frequencies generated by the simulation model were higher for white men aged>or=60 years and higher for black men aged<60 years. CONCLUSIONS: Findings indicated fairly similar patterns overall of PSA testing for blacks and whites. These similarities indicated that racial disparity in PSA testing is probably not a major factor behind current racial differences in prostate cancer mortality rates and declines. Knowledge of patterns of screening is important to an understanding of the impact of population screening on cancer incidence and mortality, but retrospective data sources have significant limitations when used to estimate these patterns of care.

Modeling the dissemination of mammography in the United States.

OBJECTIVE: This paper presents a methodology for piecing together disparate data sources to obtain a comprehensive model for the use of mammography screening in the US population for the years 1975-2000. METHODS: Two aspects of mammography usage, the age that a woman receives her first mammography and the interval between subsequent mammograms, are modeled separately. The initial dissemination of mammography is based on cross-sectional self report data from national surveys and the interval length between screening exams is fit using longitudinal mammography registry data. RESULTS: The two aspects of mammography usage are combined to simulate screening histories for individual women that are representative of the US population. Simulated mammography patterns for the years 1994-2000 were found to be similar to observed screening patterns from the state level mammography registry for Vermont. CONCLUSIONS: The model presented gives insight into screening practices over time and provides an alternative public health measure for screening usage in the US population. The comprehensive description of mammography use from its introduction represents an important first step to understanding the impact of mammography on breast cancer incidence and mortality.