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Introduction: Cancer prevalence (people alive on a certain date in a population who previously had a cancer diagnosis) is expected to increase in the United States and Europe due to improvements in survival and population aging. Examination of prevalence by phase of care allows us to identify subgroups of patients according to their care trajectories, thus allowing us to improve health care planning, resource allocation, and calculation of costs. Methods: A new method to estimate prevalence by phase of care using grouped data is illustrated. Prevalence is divided into 3 mutually exclusive phases: initial, continuing, and end-of-life. An application to US and Italian data is applied to prevalent cases diagnosed with colon–rectum, stomach, lung, or breast cancer. Results: The distribution of phase of care prevalence estimated by cancer type and sex and results from the two datasets are very similar. Most survivors are in the continuing phase; the end-of-life phase is larger for cancers with worse prognosis. All phases prevalence is generally higher in the Italian than in the US dataset, except for lung cancer in women, where prevalence proportion in the Italian dataset is 30% lower than in the United States. Discussion: Incidence, survival, and population age structure are the main determinants of prevalence and they can affect differences in all phases of prevalence, as well as in discrete phases. Incidence is the most influential determinant. Ours is the first study that compares prevalence by phase of care between two populations in Italy and the United States. Despite great differences in health care management in the two countries, we found extremely similar distribution of survivors by phase of care for most cancer sites under study.

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.

BACKGROUND: Investigations of suspected cancer clusters are resource intensive and rarely identify true clusters: among 428 publicly reported US investigations during 1990–2011, only 1 etiologic cluster was identified. In 2013, the Cancer Data Registry of Idaho (CDRI) was contacted regarding a suspected cancer cluster at a worksite (Cluster A) and among an occupational cohort (Cluster B). We investigated to determine whether these were true clusters. METHODS: We derived investigation cohorts for Cluster A from facility-provided employee records and for Cluster B from professional licensing records. We used Registry Plus™ Link Plus to conduct probabilistic linkage of cohort members to the CDRI registry and completed matching through manual review by using LexisNexis®, Accurint®, and the Social Security Death Index. We calculated standardized incidence ratios (SIR) using the MP-SIR session type in SEER*Stat and Idaho and US referent populations. RESULTS: For Cluster A, we identified 34 cancer cases during 9,689 person-years; compared with Idaho and US rates, 95% CIs for SIRs included 1.0 for 24 of 24 primary site categories. For Cluster B, we identified 78 cancer cases during 15,154 person-years; compared with Idaho rates, 95% CI for SIRs included 1.0 for 23 of 24 primary site categories and was less than 1.0 for lung and bronchus cancers, and compared with US rates, 95% CI for SIRs included 1.0 for 22 of 24 primary site categories and was less than 1.0 for lung and bronchus and colorectal cancers. CONCLUSION: We identified no statistically significant excess in cancer incidence in either cohort. SEER*Stat’s MP-SIR is an efficient tool for performing SIR assessments, a Centers for Disease Control and Prevention/Council of State and Territorial Epidemiologists–recommended step when investigating suspected cancer clusters.

Background: Cancer survivors represent a growing population, heterogeneous in their need for medical care, psychosocial support, and practical assistance. To inform survivorship research and practice, this manuscript will describe the prevalent population of cancer survivors in terms of overall numbers and prevalence by cancer site and time since diagnosis. Methods: Incidence and survival data from 1975–2007 were obtained from the Surveillance, Epidemiology, and End Results Program and population projections from the United States Census Bureau. Cancer prevalence for 2012 and beyond was estimated using the Prevalence Incidence Approach Model, assuming constant future incidence and survival trends but dynamic projections of the U.S. population. Results: As of January 1, 2012, approximately 13.7 million cancer survivors were living in the United States with prevalence projected to approach 18 million by 2022. Sixty-four percent of this population have survived 5 years or more; 40% have survived 10 years or more; and 15% have survived 20 years or more after diagnosis. Over the next decade, the number of people who have lived 5 years or more after their cancer diagnosis is projected to increase approximately 37% to 11.9 million. Conclusions: A coordinated agenda for research and practice is needed to address cancer survivors' long-term medical, psychosocial, and practical needs across the survivorship trajectory. Impact: Prevalence estimates for cancer survivors across the survivorship trajectory will inform the national research agenda as well as future projections about the health service needs of this population. Cancer Epidemiol Biomarkers Prev; 22(4); 561–70. ©2013 AACR.

Although there has been considerable progress in reducing cancer incidence in the United States, the number of cancer survivors continues to increase due to the aging and growth of the population and improvements in survival rates. As a result, it is increasingly important to understand the unique medical and psychosocial needs of survivors and be aware of resources that can assist patients, caregivers, and health care providers in navigating the various phases of cancer survivorship. To highlight the challenges and opportunities to serve these survivors, the American Cancer Society and the National Cancer Institute estimated the prevalence of cancer survivors on January 1, 2012 and January 1, 2022, by cancer site. Data from Surveillance, Epidemiology, and End Results (SEER) registries were used to describe median age and stage at diagnosis and survival; data from the National Cancer Data Base and the SEER-Medicare Database were used to describe patterns of cancer treatment. An estimated 13.7 million Americans with a history of cancer were alive on January 1, 2012, and by January 1, 2022, that number will increase to nearly 18 million. The 3 most prevalent cancers among males are prostate (43%), colorectal (9%), and melanoma of the skin (7%), and those among females are breast (41%), uterine corpus (8%), and colorectal (8%). This article summarizes common cancer treatments, survival rates, and posttreatment concerns and introduces the new National Cancer Survivorship Resource Center, which has engaged more than 100 volunteer survivorship experts nationwide to develop tools for cancer survivors, caregivers, health care professionals, advocates, and policy makers.

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. Cancer 2012; © 2012 American Cancer Society.

BACKGROUND: Breast cancer continues to place a significant burden on the healthcare system. Regional prevalence measures are instrumental in the development of cancer control policies. Very few population-based cancer registries are able to provided local, long-term incidence and follow-up information that permits the direct calculation of prevalence. Model-based prevalence estimates are an alternative when this information is lacking or incomplete. The current work represents a comprehensive collection of female breast cancer prevalence from 2005 to 2015 in the United States and the District of Columbia (DC). METHODS: Breast cancer prevalence estimates were derived from state-specific cancer mortality and survival data using a statistical package called the Mortality-Incidence Analysis Model or MIAMOD. Cancer survival models were derived from the Surveillance, Epidemiology, and End Results Program data and were adjusted to represent state-specific survival. Comparisons with reported incidence for 39 states and DC had validated estimates. RESULTS: By the year 2010, 2.9 million breast cancer survivors are predicted in the US, equaling 1.85% of the female population. Large variability in prevalent percentages was reported between states, ranging from 1.4% to 2.4% in 2010. Geographic variability was reduced when calculating age-standardized prevalence proportions or cancer survivors by disease duration, including 0 to 2 years and 2 to 5 years. The residual variability in age-adjusted prevalence was explained primarily by the state-specific, age-adjusted breast cancer incidence rates. State-specific breast cancer survivors are expected to increase from 16% to 51% in the decennium from 2005 to 2015 and by 31% at the national level. CONCLUSIONS: To the authors' knowledge, the current study is the first to provide systematic estimations of breast cancer prevalence in all US states through 2015. The estimated levels and time trends were consistent with the available population-based data on breast cancer incidence, prevalence, and population aging. Cancer 2009. © 2009 American Cancer Society.

Objective: The present study was designed to estimate the number of and describe the pattern of disease among cancer survivors living with a history of multiple malignant tumors in the United States. Methods: Incidence and follow-up data from the Surveillance, Epidemiology, and End Results program (1975-2001) were used to calculate the number of survivors with more than one malignant primary at January 1, 2002. U.S. prevalence counts were calculated by multiplying the age, sex, and race-specific prevalence proportions from the Surveillance, Epidemiology, and End Results program by the corresponding U.S. populations. Results: We estimate that 756,467 people in the United States have been affected by cancer more than once between 1975 and 2001, representing almost 8% of the current cancer survivor population. Women whose first primary in that period was breast cancer represent 25% of survivors with multiple cancers, followed by men and women (15%) whose first primary was colorectal cancer and men (13%) whose first primary was prostate cancer. Discussion: The findings in this report have important implications for public health practice. With individuals diagnosed with cancer living longer and the aging of the U.S. population, the number who will develop multiple malignancies is expected to increase. As a consequence, there is a growing need to promote effective cancer screening along with healthy life-styles among these at-risk populations if we are to ensure optimal physical and psychosocial well-being of these long-term cancer survivors and their families. Efforts to design and evaluate effective, efficient, and equitable approaches to surveillance for second malignancies will be critical in reducing the national burden of cancer. (Cancer Epidemiol Biomarkers Prev 2007;16(3):566–71)

There is little debate that measures of survival are a valuable tool available to clinicians, epidemiologists, and public health professionals (1). First proposed by Ederer et al. (2) relative survival compares the survival probabilities of a diseased population (ie, cancer patients) to the survival probabilities of the general population. The resulting value, known as relative survival, is the ratio of observed survival to expected survival which represents the excess mortality associated with a cancer diagnosis. Relative survival is often used as the primary measure in population-based descriptive studies as in general it provides a more reliable estimate of the net survival from cancer than cause-specific survival because it does not rely on death certificate information, which is generally prone to errors in the coding of cause of death and has been shown to have high degrees of misclassification for cancer causes of death (3–5). Unlike crude probabilities of death, net survival represents cancer survival in the absence of competing risks (6). In this issue, Howlader et al.(7) and Mariotto et al. (8) further discuss differences between net and crude survival measures. A key challenge of relative survival is in choosing the population with which to compare to the cancer cohort or in choosing the best life tables to represent the background mortality of the study population. There is a growing body of research questioning the accuracy of relative survival estimates that use life tables from populations that are either not comparable to the cancer cohort [eg, background mortality risk is substantially higher or lower than the cancer cohort, misclassification or unmeasured factors such as race/ethnicity that contribute to differential death rates in the comparison population (9–11)] or when the reference population has a high rate of deaths due to cancer (12). In the United States, researchers using cancer surveillance data from the Surveillance Epidemiology and End Results (SEER) Program often use the US population life tables as the reference group for relative survival estimates, matched by age, sex, race, and calendar year to the cancer cohort (13). The use of US population life tables in and of itself is not necessarily a problem if the cancer cohort has comparable background death rates and characteristics. However, we know that background mortality varies by sex, age, race, and geographic areas as well as socioeconomic status (SES) (14–16). The use of national life tables to calculate state-specific or regional survival has overestimated relative survival in states with lower background death rates and underestimated relative survival in states with higher background death rates (9). Baili et al. (9) reported that even after matching on age, sex, year, and race, relative survival for several US states and regional registry populations were systematically higher when using US-matched life tables compared to the relative survival estimates using SLT [aka: the CONCORD approach (9)]. The only exception was found in Louisiana, which had higher US-matched relative survival estimates as compared to its state-matched relative survival estimates. The authors attributed these differences to the lower background death rates in all states/regions in their study with the exception of Louisiana, which had higher background death rates. Moreover, for early stage prostate and breast cancer, relative survival has been shown to be higher than 100%, indicating that the life tables may not be appropriate for representing survival from other causes when examining cancer survival for these populations (9). With the recent release of the 2000 state decennial life tables by the National Center for Health Statistics (NCHS) (17), this study expands on the work by Baili et al. (9) by comparing five-year relative survival using state- or regionally-matched life tables to US-matched five-year relative survival, focusing on a more contemporary cohort of cancer patients diagnosed from 2000 to 2009. We also examine the underlying characteristics of each state/region to identify potential root causes of variations in survival estimates and assess the variations in survival estimates by age, race, and cancer site for lung and bronchus cancer, colorectal cancer, prostate cancer, and female breast cancer.

National cancer incidence trends are presented for eight Asian American groups: Asian Indians/Pakistanis, Chinese, Filipinos, Japanese, Kampucheans, Koreans, Laotians, and Vietnamese.Cancer incidence data from 1990 through 2008 were obtained from 13 Surveillance, Epidemiology, End Results (SEER) registries. Incidence rates from 1990 through 2008 and average percentage change were computed using SEER*Stat and Joinpoint software. The annual percentage change (APC) in incidence rates was estimated with 95% confidence intervals (95% CIs) calculated for both the rate and APC estimates. Rates for non-Hispanic whites are presented for comparison.Prostate cancer was the most common malignancy among most groups, followed by lung, colorectal, liver, and stomach cancers. Breast cancer was generally the most common cancer in women, followed by colorectal and lung cancers; liver, cervix, thyroid, and stomach cancers also ranked highly. Among men, increasing trends were observed for prostate (Asian Indians and Pakistanis: APC 1990-2003 = 2.2, 95% CI = 0.3 to 4.1; Filipinos: APC 1990-1994 = 19.0, 95% CI = 4.5 to 35.4; Koreans: APC 1990-2008 = 2.9, 95% CI = 1.8 to 4.0), colorectal (Koreans: APC 1990-2008 = 2.2, 95% CI = 0.9 to 3.5), and liver cancers (Filipinos: APC 1990-2008 = 1.6, 95% CI = 0.4 to 2.7; Koreans: APC 1990-2006 = 2.1, 95% CI = 0.4 to 3.7; Vietnamese: APC 1990-2008 = 1.6, 95% CI = 0.3 to 2.8), whereas lung and stomach cancers generally remained stable or decreased. Among women, increases were observed for uterine cancer (Asian Indians: APC 1990-2008 = 3.0, 95% CI = 0.3 to 5.8; Chinese: APC 2004-2008 = 7.0, 95% CI = 1.4 to 12.9; Filipina: APC 1990-2008 = 3.0, 95% CI = 2.4 to 3.7; Japanese: APC 1990-2008 = 1.1, 95% CI = 0.1 to 2.0), colorectal cancer (Koreans: APC 1990-2008 = 2.8, 95% CI = 1.7 to 3.9; Laotians: APC: 1990-2008 = 5.9, 95% CI = 4.0 to 7.7), lung cancer (Filipinas: APC 1990-2008 = 2.1, 95% CI = 1.4 to 2.8; Koreans: APC 1990-2008 = 2.1, 95% CI = 0.6 to 3.6), thyroid cancer (Filipinas: APC 1990-2008 = 2.5, 95% CI = 1.7 to 3.3), and breast cancer in most groups (APC 1990-2008 from 1.2 among Vietnamese and Chinese to 4.7 among Koreans). Decreases were observed for stomach (Chinese and Japanese), colorectal (Chinese), and cervical cancers (Laotians and Vietnamese).These data fill a critical knowledge gap concerning the cancer experience of Asian American groups and highlight where increased preventive, screening, and surveillance efforts are needed-in particular, lung cancer among Filipina and Korean women and Asian Indian/Pakistani men, breast cancer among all women, and liver cancer among Vietnamese, Laotian, and Kampuchean women and Filipino, Kampuchean, and Vietnamese men.

Results Statistically significant declining trends were found in Native Hawaiians, in men for lung and stomach cancers (APC = –2.3%; 95% CI = –3.3 to –1.3; and APC = –3.8%; 95% CI = –6.0 to –1.6, respectively), and in women for breast cancer (APC = –4.1%; 95% CI = –5.7 to –2.5) since 1998 and lung cancer (APC = –6.4%; 95% CI = –10.7 to –1.8) since 2001. Rising incidence trends were experienced by Samoans, especially by Samoan women for breast (APC = 2.7%; 95% CI = 0.9 to 4.5) and uterus (APC = 7.3%; 95% CI = 6.2 to 8.4) cancers. With limited data, Guamanians/Chamorros demonstrated lower, but increasing, incidence rates than other NHOPIs.

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. Cancer 2012. © 2012 American Cancer Society.

Purpose: To estimate the number of individuals in the United States diagnosed with cancer as children (ages 0-19 years) as of 2005, with a focus on those surviving for >30 years. Methods: To estimate the national prevalence of survivors of childhood cancers, we used data from the Surveillance Epidemiology and End Results program from 1975 to 2004. Long-term childhood cancer survivors, diagnosed before 1975, were estimated using incidence and survival models extrapolated into years before 1975. Results: We estimated that there are a total of 328,652 survivors of childhood cancer in the United States as of January 1, 2005, of these, 24% have survived >30 years since diagnosis. The cancer sites with the largest number of survivors are brain (51,650), acute lymphoblastic leukemia (49,271), germ cell tumors (34,169), and Hodgkin lymphoma (31,598). Sites with higher proportions of survivors diagnosed >30 years ago are germ cell (43%), soft tissue (38%), renal (34%), and bone (26%). Historical trends from Connecticut data show major improvements in survival for all of the childhood cancer sites. Conclusion: The number of survivors of childhood cancers is expected to increase in the future consequent to the lifesaving advances in treatment introduced after 1970, especially for acute lymphoblastic leukemia. Because this population is at increased risk for illness-related morbidity and mortality, appreciating the number of survivors who were treated as children is important both to determining the national cancer burden and planning for the future health care needs of these individuals. (Cancer Epidemiol Biomarkers Prev 2009;18(4):1033–40)

Cancer prevalence is the proportion of people in a population diagnosed with cancer in the past and still alive. One way to estimate prevalence is via population-based registries, where data on diagnosis and life status of all incidence cases occurring in the covered population are collected. In this paper, a method to estimate the complete prevalence and its variance from population-based registries is presented. In order to obtain unbiased estimates of the complete prevalence, its calculation can be thought as made by three steps. Step 1 counts the incidence cases diagnosed during the period of registration and still alive. Step 2 estimates the expected number of survivors among cases lost to follow-up. Step 3 estimates the complete prevalence by taking into account cases diagnosed before the start of registration. The combination of steps 1+2 is defined as the counting method, to estimate the limited duration prevalence; step 3 is the completeness index method, to estimate the complete prevalence. For early established registries, steps 1+2 are more important than step 3, because observation time is long enough to include all past diagnosed cases still alive in the prevalence data. For more recently established registries, step 3 is by far the most critical because a large part of prevalence might have been diagnosed before the period of registration (Corazziari I, Mariotto A, Capocaccia R. Correcting the completeness bias of observed prevalence. Tumori 1999; 85: 370-81). The work by Clegg LX, Gail MH, Feuer EJ. Estimating the variance of disease-prevalence estimates from population-based registries. Biometrics 2002; 55: 1137-44. considers the problem of the variability of the estimated prevalence up to step 2. To our knowledge, no other work has considered the variability induced by correcting for the unobserved cases diagnosed before the period of registration, crucial to estimate the prevalence in recent registries. An analytic approach is considered to calculate the variance of step 3. A unified expression for the variance of the prevalence allowing for steps 1 through 3 is obtained. Some applications to cancer data are presented.