Cancer prevalence by phase of care: an indicator for assessing health service needs.

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.

Cancer survivors in the United States: prevalence across the survivorship trajectory and implications for care.

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 treatment and survivorship statistics, 2012.

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.

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.

Age-Adjusted US Cancer Death Rate Predictions.

The likelihood of developing cancer during one's lifetime is approximately one in two for men and one in three for women in the United States. Cancer is the second-leading cause of death and accounts for one in every four deaths. Evidence-based policy planning and decision making by cancer researchers and public health administrators are best accomplished with up-to-date age-adjusted site-specific cancer death rates. Because of the 3-year lag in reporting, forecasting methodology is employed here to estimate the current year rates based on complete observed death data up through three years prior to the current year. The authors expand the State Space Model (SSM) statistical methodology currently in use by the American Cancer Society (ACS) to predict age-adjusted cancer death rates for the current year. These predictions are compared with those from the previous Proc Forecast ACS method and results suggest the expanded SSM performs well.

Comparison of tests for spatial heterogeneity on data with global clustering patterns and outliers.

BACKGROUND: The ability to evaluate geographic heterogeneity of cancer incidence and mortality is important in cancer surveillance. Many statistical methods for evaluating global clustering and local cluster patterns are developed and have been examined by many simulation studies. However, the performance of these methods on two extreme cases (global clustering evaluation and local anomaly (outlier) detection) has not been thoroughly investigated. METHODS: We compare methods for global clustering evaluation including Tango's Index, Moran's I, and Oden's I*(pop); and cluster detection methods such as local Moran's I and SaTScan elliptic version on simulated count data that mimic global clustering patterns and outliers for cancer cases in the continental United States. We examine the power and precision of the selected methods in the purely spatial analysis. We illustrate Tango's MEET and SaTScan elliptic version on a 1987-2004 HIV and a 1950-1969 lung cancer mortality data in the United States. RESULTS: For simulated data with outlier patterns, Tango's MEET, Moran's I and I*(pop) had powers less than 0.2, and SaTScan had powers around 0.97. For simulated data with global clustering patterns, Tango's MEET and I*(pop) (with 50% of total population as the maximum search window) had powers close to 1. SaTScan had powers around 0.7-0.8 and Moran's I has powers around 0.2-0.3. In the real data example, Tango's MEET indicated the existence of global clustering patterns in both the HIV and lung cancer mortality data. SaTScan found a large cluster for HIV mortality rates, which is consistent with the finding from Tango's MEET. SaTScan also found clusters and outliers in the lung cancer mortality data. CONCLUSION: SaTScan elliptic version is more efficient for outlier detection compared with the other methods evaluated in this article. Tango's MEET and Oden's I*(pop) perform best in global clustering scenarios among the selected methods. The use of SaTScan for data with global clustering patterns should be used with caution since SatScan may reveal an incorrect spatial pattern even though it has enough power to reject a null hypothesis of homogeneous relative risk. Tango's method should be used for global clustering evaluation instead of SaTScan.

Long-term survivors of childhood cancers in the United States.

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.

Breast cancer survivors in the United States: geographic variability and time trends, 2005-2015.

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. (c) 2009 American Cancer Society.

A new method of estimating United States and state-level cancer incidence counts for the current calendar year.

The American Cancer Society (ACS) has published the estimated number of new cancer cases and deaths in the current year for the United States that are commonly used by cancer control planners and the media. The methods used to produce these estimates have changed over the years as data (incidence) and statistical models improved. In this paper we present a new method that uses statistical models of cancer incidence that incorporate potential predictors of spatial and temporal variation of cancer occurrence and that account for delay in case reporting and then projects these estimated numbers of cases ahead 4 years using a piecewise linear (joinpoint) regression method. Based on evidence presented here that the new method produces more accurate estimates of the number of new cancer cases for years and areas for which data are available for comparison, the ACS has elected to use it to estimate the number of new cancer cases in Cancer Facts & Figures 2007 and in Cancer Statistics, 2007.

On Using Truncated Sequential Probability Ratio Test Boundaries for Monte Carlo Implementation of Hypothesis Tests.

When designing programs or software for the implementation of Monte Carlo (MC) hypothesis tests, we can save computation time by using sequential stopping boundaries. Such boundaries imply stopping resampling after relatively few replications if the early replications indicate a very large or very small p-value. We study a truncated sequential probability ratio test (SPRT) boundary and provide a tractable algorithm to implement it. We review two properties desired of any MC p-value, the validity of the p-value and a small resampling risk, where resampling risk is the probability that the accept/reject decision will be different than the decision from complete enumeration. We show how the algorithm can be used to calculate a valid p-value and confidence intervals for any truncated SPRT boundary. We show that a class of SPRT boundaries is minimax with respect to resampling risk and recommend a truncated version of boundaries in that class by comparing their resampling risk (RR) to the RR of fixed boundaries with the same maximum resample size. We study the lack of validity of some simple estimators of p-values and offer a new simple valid p-value for the recommended truncated SPRT boundary. We explore the use of these methods in a practical example and provide the MChtest R package to perform the methods.

Estimating the variance of cancer prevalence from population-based registries.

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.

A new method of predicting US and state-level cancer mortality counts for the current calendar year.

Every January for more than 40 years, the American Cancer Society (ACS) has estimated the total number of cancer deaths that are expected to occur in the United States and individual states in the upcoming year. In a collaborative effort to improve the accuracy of the predictions, investigators from the National Cancer Institute and the ACS have developed and tested a new prediction method. The new method was used to create the mortality predictions for the first time in Cancer Statistics, 2004 and Cancer Facts & Figures 2004. The authors present a conceptual overview of the previous ACS method and the new state-space method (SSM), and they review the results of rigorous testing to determine which method provides more accurate predictions of the observed number of cancer deaths from the years 1997 to 1999. The accuracy of the methods was compared using squared deviations (the square of the predicted minus observed values) for each of the cancer sites for which predictions are published as well as for all cancer sites combined. At the national level, the squared deviations were not consistently lower for every cancer site for either method, but the average squared deviations (averaged across cancer sites, years, and sex) was substantially lower for the SSM than for the ACS method. During the period 1997 to 1999, the ACS estimates of deaths were usually greater than the observed numbers for all cancer sites combined and for several major individual cancer sites, probably because the ACS method was less sensitive to recent changes in cancer mortality rates (and associated counts) that occurred for several major cancer sites in the early and mid 1990s. The improved accuracy of the new method was particularly evident for prostate cancer, for which mortality rates changed dramatically in the late 1980s and early 1990s. At the state level, the accuracy of the two methods was comparable. Based on these results, the ACS has elected to use the new method for the annual prediction of the number of cancer deaths at the national and state levels.