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.

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.

The Surveillance, Epidemiology, and End Results Cancer Survival Calculator SEER*CSC: validation in a managed care setting.

BACKGROUND: Nomograms for prostate and colorectal cancer are included in the Surveillance, Epidemiology, and End Results (SEER) Cancer Survival Calculator, under development by the National Cancer Institute. They are based on the National Cancer Institute's SEER data, coupled with Medicare data, to estimate the probabilities of surviving or dying from cancer or from other causes based on a set of patient and tumor characteristics. The nomograms provide estimates of survival that are specific to the characteristics of the tumor, age, race, gender, and the overall health of a patient. These nomograms have been internally validated using the SEER data. In this paper, we externally validate the nomograms using data from Kaiser Permanente Colorado. METHODS: The SEER Cancer Survival Calculator was externally validated using time-dependent area under the Receiver Operating Characteristic curve statistics and calibration plots for retrospective cohorts of 1102 prostate cancer and 990 colorectal cancer patients from Kaiser Permanente Colorado. RESULTS: The time-dependent area under the Receiver Operating Characteristic curve statistics were computed for one, three, five, seven, and 10 year(s) postdiagnosis for prostate and colorectal cancer and ranged from 0.77 to 0.89 for death from cancer and from 0.72 to 0.81 for death from other causes. The calibration plots indicated a very good fit of the model for death from cancer for colorectal cancer and for the higher risk group for prostate cancer. For the lower risk groups for prostate cancer (<10% chance of dying of prostate cancer in 10 years), the model predicted slightly worse prognosis than observed. Except for the lowest risk group for colorectal cancer, the models for death from other causes for both prostate and colorectal cancer predicted slightly worse prognosis than observed. CONCLUSIONS: The results of the external validation indicated that the colorectal and prostate cancer nomograms are reliable tools for physicians and patients to use to obtain information on prognosis and assist in establishing priorities for both treatment of the cancer and other conditions, particularly when a patient is elderly and/or has significant comorbidities. The slightly better than predicted risk of death from other causes in a health maintenance organization (HMO) setting may be due to an overall healthier population and the integrated management of disease relative to the overall population (as represented by SEER).

Health-care utilization by prognosis profile in a managed care setting: using the Surveillance, Epidemiology and End Results Cancer Survival Calculator SEER*CSC.

BACKGROUND: Accurate estimation of the probability of dying of cancer versus other causes is needed to inform goals of care for cancer patients. Further, prognosis may also influence health-care utilization. This paper describes health service utilization patterns of subgroups of prostate cancer and colorectal cancer (CRC) patients with different relative probabilities of dying of their cancer or other conditions. METHODS: A retrospective cohort of cancer patients from Kaiser Permanente Colorado were divided into three groups using the predicted probabilities of dying of cancer and other causes calculated by the nomograms in the National Cancer Institute Surveillance, Epidemiology and End Results Cancer Survival Calculator. Demographic, disease-related characteristics, and health service utilization patterns were described across subgroups. RESULTS: The cohort consisted of 2092 patients (1102 prostate cancer and 990 CRC). A new diagnosis of cancer increased utilization of cancer-related services with rates as high as 9.1/1000 person-days for prostate cancer and 36.2/1000 person-days for CRC. Little change was observed in the number of primary and other specialty care visits from prediagnosis to 1 and 2 years postdiagnosis. CONCLUSIONS: We found that although a new diagnosis of cancer increased utilization of cancer-related services for an extended time period, the timing of cancer diagnosis did not appear to affect other types of utilization. Future research should assess the reason for the lack of impact of cancer and unrelated comorbid conditions on utilization and whether desired outcomes of care were achieved.

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.

Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths.

BACKGROUND: In the National Polyp Study (NPS), colorectal cancer was prevented by colonoscopic removal of adenomatous polyps. We evaluated the long-term effect of colonoscopic polypectomy in a study on mortality from colorectal cancer. METHODS: We included in this analysis all patients prospectively referred for initial colonoscopy (between 1980 and 1990) at NPS clinical centers who had polyps (adenomas and nonadenomas). The National Death Index was used to identify deaths and to determine the cause of death; follow-up time was as long as 23 years. Mortality from colorectal cancer among patients with adenomas removed was compared with the expected incidence-based mortality from colorectal cancer in the general population, as estimated from the Surveillance Epidemiology and End Results (SEER) Program, and with the observed mortality from colorectal cancer among patients with nonadenomatous polyps (internal control group). RESULTS: Among 2602 patients who had adenomas removed during participation in the study, after a median of 15.8 years, 1246 patients had died from any cause and 12 had died from colorectal cancer. Given an estimated 25.4 expected deaths from colorectal cancer in the general population, the standardized incidence-based mortality ratio was 0.47 (95% confidence interval [CI], 0.26 to 0.80) with colonoscopic polypectomy, suggesting a 53% reduction in mortality. Mortality from colorectal cancer was similar among patients with adenomas and those with nonadenomatous polyps during the first 10 years after polypectomy (relative risk, 1.2; 95% CI, 0.1 to 10.6). CONCLUSIONS: These findings support the hypothesis that colonoscopic removal of adenomatous polyps prevents death from colorectal cancer. (Funded by the National Cancer Institute and others.).

Estimating average annual per cent change in trend analysis.

Trends in incidence or mortality rates over a specified time interval are usually described by the conventional annual per cent change (cAPC), under the assumption of a constant rate of change. When this assumption does not hold over the entire time interval, the trend may be characterized using the annual per cent changes from segmented analysis (sAPCs). This approach assumes that the change in rates is constant over each time partition defined by the transition points, but varies among different time partitions. Different groups (e.g. racial subgroups), however, may have different transition points and thus different time partitions over which they have constant rates of change, making comparison of sAPCs problematic across groups over a common time interval of interest (e.g. the past 10 years). We propose a new measure, the average annual per cent change (AAPC), which uses sAPCs to summarize and compare trends for a specific time period. The advantage of the proposed AAPC is that it takes into account the trend transitions, whereas cAPC does not and can lead to erroneous conclusions. In addition, when the trend is constant over the entire time interval of interest, the AAPC has the advantage of reducing to both cAPC and sAPC. Moreover, because the estimated AAPC is based on the segmented analysis over the entire data series, any selected subinterval within a single time partition will yield the same AAPC estimate--that is it will be equal to the estimated sAPC for that time partition. The cAPC, however, is re-estimated using data only from that selected subinterval; thus, its estimate may be sensitive to the subinterval selected. The AAPC estimation has been incorporated into the segmented regression (free) software Joinpoint, which is used by many registries throughout the world for characterizing trends in cancer rates.

Impact of socioeconomic status on cancer incidence and stage at diagnosis: selected findings from the surveillance, epidemiology, and end results: National Longitudinal Mortality Study.

BACKGROUND: Population-based cancer registry data from the Surveillance, Epidemiology, and End Results (SEER) Program at the National Cancer Institute (NCI) are mainly based on medical records and administrative information. Individual-level socioeconomic data are not routinely reported by cancer registries in the United States because they are not available in patient hospital records. The U.S. representative National Longitudinal Mortality Study (NLMS) data provide self-reported, detailed demographic and socioeconomic data from the Social and Economic Supplement to the Census Bureau's Current Population Survey (CPS). In 1999, the NCI initiated the SEER-NLMS study, linking the population-based SEER cancer registry data to NLMS data. The SEER-NLMS data provide a new unique research resource that is valuable for health disparity research on cancer burden. We describe the design, methods, and limitations of this data set. We also present findings on cancer-related health disparities according to individual-level socioeconomic status (SES) and demographic characteristics for all cancers combined and for cancers of the lung, breast, prostate, cervix, and melanoma. METHODS: Records of cancer patients diagnosed in 1973-2001 when residing 1 of 11 SEER registries were linked with 26 NLMS cohorts. The total number of SEER matched cancer patients that were also members of an NLMS cohort was 26,844. Of these 26,844 matched patients, 11,464 were included in the incidence analyses and 15,357 in the late-stage diagnosis analyses. Matched patients (used in the incidence analyses) and unmatched patients were compared by age group, sex, race, ethnicity, residence area, year of diagnosis, and cancer anatomic site. Cohort-based age-adjusted cancer incidence rates were computed. The impact of socioeconomic status on cancer incidence and stage of diagnosis was evaluated. RESULTS: Men and women with less than a high school education had elevated lung cancer rate ratios of 3.01 and 2.02, respectively, relative to their college educated counterparts. Those with family annual incomes less than $12,500 had incidence rates that were more than 1.7 times the lung cancer incidence rate of those with incomes $50,000 or higher. Lower income was also associated with a statistically significantly increased risk of distant-stage breast cancer among women and distant-stage prostate cancer among men. CONCLUSIONS: Socioeconomic patterns in incidence varied for specific cancers, while such patterns for stage were generally consistent across cancers, with late-stage diagnoses being associated with lower SES. These findings illustrate the potential for analyzing disparities in cancer outcomes according to a variety of individual-level socioeconomic, demographic, and health care characteristics, as well as by area measures available in the linked database.

Cancer incidence and mortality patterns among specific Asian and Pacific Islander populations in the U.S.

OBJECTIVES: We report cancer incidence, mortality, and stage distributions among Asians and Pacific Islanders (API) residing in the U.S. and note health disparities, using the cancer experience of the non-Hispanic white population as the referent group. New databases added to publicly available SEER*Stat software will enable public health researchers to further investigate cancer patterns among API groups. METHODS: Cancer diagnoses among API groups occurring from 1 January 1998 to 31 December 2002 were included from 14 Surveillance, Epidemiology, and End Results (SEER) Program state and regional population-based cancer registries covering 54% of the U.S. API population. Cancer deaths were included from the seven states that report death information for detailed API groups and which cover over 68% of the total U.S. API population. Using detailed racial/ethnic population data from the 2000 decennial census, we produced incidence rates centered on the census year for Asian Indians/Pakistanis, Chinese, Filipinos, Guamanians, Native Hawaiians, Japanese, Kampucheans, Koreans, Laotians, Samoans, Tongans, and Vietnamese. State vital records offices do not report API deaths separately for Kampucheans, Laotians, Pakistanis, and Tongans, so mortality rates were analyzed only for the remaining API groups. RESULTS: Overall cancer incidence rates for the API groups tended be lower than overall rates for non-Hispanic whites, with the exception of Native Hawaiian women (All cancers rate = 488.5 per 100,000 vs. 448.5 for non-Hispanic white women). Among the API groups, overall cancer incidence and death rates were highest for Native Hawaiian and Samoan men and women due to high rates for cancers of the prostate, lung, and colorectum among Native Hawaiian men; cancers of the prostate, lung, liver, and stomach among Samoan men; and cancers of the breast and lung among Native Hawaiian and Samoan women. Incidence and death rates for cancers of the liver, stomach, and nasopharynx were notably high in several of the API groups and exceeded rates generally seen for non-Hispanic white men and women. Incidence rates were lowest among Asian Indian/Pakistani and Guamanian men and women and Kampuchean women. Asian Indian and Guamanian men and women also had the lowest cancer death rates. Selected API groups had less favorable distributions of stage at diagnosis for certain cancers than non-Hispanic whites. CONCLUSIONS: Possible disparities in cancer incidence or mortality between specific API groups in our study and non-Hispanic whites (referent group) were identified for several cancers. Unfavorable patterns of stage at diagnosis for cancers of the colon and rectum, breast, cervix uteri, and prostate suggest a need for cancer control interventions in selected groups. The observed variation in cancer patterns among API groups indicates the importance of monitoring these groups separately, as these patterns may provide etiologic clues that could be investigated by analytic epidemiological studies.

Quality of race, Hispanic ethnicity, and immigrant status in population-based cancer registry data: implications for health disparity studies.

Population-based cancer registry data from the Surveillance, Epidemiology, and End Results (SEER) Program at the National Cancer Institute are based on medical records and administrative information. Although SEER data have been used extensively in health disparities research, the quality of information concerning race, Hispanic ethnicity, and immigrant status has not been systematically evaluated. The quality of this information was determined by comparing SEER data with self-reported data among 13,538 cancer patients diagnosed between 1973-2001 in the SEER--National Longitudinal Mortality Study linked database. The overall agreement was excellent on race (kappa = 0.90, 95% CI = 0.88-0.91), moderate to substantial on Hispanic ethnicity (kappa = 0.61, 95% CI = 0.58-0.64), and low on immigrant status (kappa = 0.21. 95% CI = 0.10, 0.23). The effect of these disagreements was that SEER data tended to under-classify patient numbers when compared to self-identifications, except for the non-Hispanic group which was slightly over-classified. These disagreements translated into varying racial-, ethnic-, and immigrant status-specific cancer statistics, depending on whether self-reported or SEER data were used. In particular, the 5-year Kaplan-Meier survival and the median survival time from all causes for American Indians/Alaska Natives were substantially higher when based on self-classification (59% and 140 months, respectively) than when based on SEER classification (44% and 53 months, respectively), although the number of patients is small. These results can serve as a useful guide to researchers contemplating the use of population-based registry data to ascertain disparities in cancer burden. In particular, the study results caution against evaluating health disparities by using birthplace as a measure of immigrant status and race information for American Indians/Alaska Natives.

Tissues from population-based cancer registries: a novel approach to increasing research potential.

Population-based cancer registries, such as those included in the Surveillance, Epidemiology, and End-Results (SEER) Program, offer tremendous research potential beyond traditional surveillance activities. We describe the expansion of SEER registries to gather formalin-fixed, paraffin-embedded tissue from cancer patients on a population basis. Population-based tissue banks have the advantage of providing an unbiased sampling frame for evaluating the public health impact of genes or protein targets that may be used for therapeutic or diagnostic purposes in defined communities. Such repositories provide a unique resource for testing new molecular classification schemes for cancer, validating new biologic markers of malignancy, prognosis and progression, assessing therapeutic targets, and measuring allele frequencies of cancer-associated genetic polymorphisms or germline mutations in representative samples. The assembly of tissue microarrays will allow for the use of rapid, large-scale protein-expression profiling of tumor samples while limiting depletion of this valuable resource. Access to biologic specimens through SEER registries will provide researchers with demographic, clinical, and risk factor information on cancer patients with assured data quality and completeness. Clinical outcome data, such as disease-free survival, can be correlated with previously validated prognostic markers. Furthermore, the anonymity of the study subject can be protected through rigorous standards of confidentiality. SEER-based tissue resources represent a step forward in true, population-based tissue repositories of tumors from US patients and may serve as a foundation for molecular epidemiology studies of cancer in this country.

CK20 and CK7 protein expression in colorectal cancer: demonstration of the utility of a population-based tissue microarray.

The ability to use archival tissue to test externally valid hypotheses of carcinogenesis is dependent on the availability of population-based samples of cancer tissue. Tissue microarrays (TMAs) provide an efficient format for developing population-based samples of tissue. A TMA was constructed consisting of archival tissue from patients diagnosed with invasive colorectal cancer in the state of Hawaii in 1995. The population representativeness of the TMA was evaluated by comparing patient and clinical characteristics of TMA cases to that of all cases of colorectal carcinoma diagnosed statewide in 1995. Cytokeratin 20 (CK20) and cytokeratin 7 (CK7) immunohistochemistry was used to validate the utility of the TMA, and the expression of these proteins was correlated with patient and tumor characteristics. The TMA comprised tissue specimens from 286 patients representing 47% of all invasive cases diagnosed statewide in 1995. TMA cases were comparable to all invasive colorectal cases statewide with respect to age, sex, race/ethnicity, anatomic site, and survival. There were some differences between TMA cases and all cases with respect to tumor stage, histological classification, and treatment. There were significant differences in the relative expression of CK20 and CK7 proteins between malignant and normal tissues and by tumor stage. Advanced cancers were more likely to have CK20+/cytokeratin 7+ (CK7+) profiles than early-stage cancers, which were predominantly CK20+/cytokeratin 7- (CK7-). CK7+ expression was not correlated with anatomic location of carcinomas. This well-characterized TMA offers a powerful tool for testing hypotheses regarding colorectal carcinogenesis, including the identification of potential markers of neoplastic development and progression.

Persistent area socioeconomic disparities in U.S. incidence of cervical cancer, mortality, stage, and survival, 1975-2000.

BACKGROUND: Temporal cervical cancer incidence and mortality patterns and ethnic disparities in patient survival and stage at diagnosis in relation to socioeconomic deprivation measures have not been well studied in the United States. The current article analyzed temporal area socioeconomic inequalities in U.S. cervical cancer incidence, mortality, stage, and survival. METHODS: County and census tract poverty and education variables from the 1990 census were linked to U.S. mortality and Surveillance, Epidemiology, and End Results cancer incidence data from 1975 to 2000. Age-adjusted incidence and mortality rates and 5-year cause-specific survival rates were calculated for each socioeconomic group and differences in rates were tested for statistical significance at the 0.05 level. RESULTS: Substantial area socioeconomic gradients in both incidence and mortality were observed, with inequalities in cervical cancer persisting against a backdrop of declining rates. Cervical cancer incidence and mortality rates increased with increasing poverty and decreasing education levels for the total population as well as for non-Hispanic white, black, American Indian, Asian/Pacific Islander, and Hispanic women. Patients in lower socioeconomic census tracts had significantly higher rates of late-stage cancer diagnosis and lower rates of cancer survival. Even after controlling for stage, significant differences in survival remained. The 5-year survival rate among women diagnosed with distant-stage cervical cancer was approximately 30% lower in low than in high socioeconomic census tracts. CONCLUSIONS: Census-based socioeconomic measures such as area poverty and education levels could serve as important surveillance tools for monitoring temporal trends in cancer-related health inequalities and targeting interventions.

Cancer survival and incidence from the Surveillance, Epidemiology, and End Results (SEER) program.

An overview of data on cancer at all sites combined and on selected, frequently occurring cancers is presented. Descriptive cancer statistics include average annual Surveillance, Epidemiology, and End Results (SEER) Program incidence, U.S. mortality and median age at diagnosis, and death for the period 1996-2000. Changes during the time period 1992-2000 are summarized by the annual percent change in SEER incidence and U.S. mortality data for this period. Five-year relative survival for selected cancers is examined by stage at diagnosis, based on data from 1990-1999. In addition, 5-year conditional survival for patients already surviving for 1-3 years after diagnosis is discussed as well as relative survival for other time periods. These measures may be more meaningful for clinical management and prognosis than 5-year relative survival from time of diagnosis. The likelihood of developing cancer during one's lifetime is 1 in 2 for males and 1 in 3 for females, based on 1998-2000 data. It is estimated that approximately 9.6 million people in the U.S. who have had a diagnosis of cancer are alive. Five-year relative survival varies greatly by cancer site and stage at diagnosis, and tends to increase with time since diagnosis. The median age at cancer diagnosis is 68 for men and 65 for women. The 5-year relative survival rate for persons diagnosed with cancer is 62.7%, with variation by cancer site and stage at diagnosis. For patients diagnosed with cancers of the prostate, female breast, corpus uteri, and urinary bladder, the relative survival rate at 8 years is over 75%.

Annual report to the nation on the status of cancer, 1975-2000, featuring the uses of surveillance data for cancer prevention and control.

BACKGROUND: The American Cancer Society, the Centers for Disease Control and Prevention (CDC), the National Cancer Institute (NCI), and the North American Association of Central Cancer Registries (NAACCR) collaborate annually to update cancer rates and trends in the United States. This report updates statistics on lung, female breast, prostate, and colorectal cancers and highlights the uses of selected surveillance data to assist development of state-based cancer control plans. METHODS: Age-adjusted incidence rates from 1996 through 2000 are from state and metropolitan area cancer registries that met NAACCR criteria for highest quality. Death rates are based on underlying cause-of-death data. Long-term trends and rates for major racial and ethnic populations are based on NCI and CDC data. Incidence trends from 1975 through 2000 were adjusted for reporting delays. State-specific screening and risk factor survey data are from the CDC and other federal and private organizations. RESULTS: Cancer incidence rates for all cancer sites combined increased from the mid-1970s through 1992 and then decreased from 1992 through 1995. Observed incidence rates for all cancers combined were essentially stable from 1995 through 2000, whereas the delay-adjusted trend showed an increase that had borderline statistical significance (P =.05). Increases in the incidence rates of breast cancer in women and prostate cancer in men offset a long-term decrease in lung cancer in men. Death rates for all cancer sites combined decreased beginning in 1994 and stabilized from 1998 through 2000, resulting in part from recent revisions in cause-of-death codes. Death rates among men continued to decline throughout the 1990s, whereas trends in death rates among women were essentially unchanged from 1998 through 2000. Analysis of state data for the leading cancers revealed mixed progress in achieving national objectives for improving cancer screening, risk factor reduction, and decreases in mortality. CONCLUSIONS: Overall cancer incidence and death rates began to stabilize in the mid- to late 1990s. The recent increase in the delay-adjusted trend will require monitoring with additional years of data. Further reduction in the burden of cancer is possible but will require the continuation of strong federal, state, local, and private partnerships to increase dissemination of evidence-based cancer control programs to all segments of the population.

Impact of reporting delay and reporting error on cancer incidence rates and trends.

BACKGROUND: Cancer incidence rates and trends are a measure of the cancer burden in the general population. We studied the impact of reporting delay and reporting error on incidence rates and trends for cancers of the female breast, colorectal, lung/bronchus, prostate, and melanoma. METHODS: Based on statistical models, we obtained reporting-adjusted (i.e., adjusted for both reporting delay and reporting error) case counts for each diagnosis year beginning in 1981 using reporting information for patients diagnosed with cancer in 1981-1998 from nine cancer registries that participate in the Surveillance, Epidemiology, and End Results (SEER) program. Joinpoint linear regression was used for trend analysis. All statistical tests are two-sided. RESULTS: Initial incidence case counts (i.e., after the standard 2-year delay) accounted for only 88%-97% of the estimated final counts; it would take 4-17 years for 99% or more of the cancer cases to be reported. The percent change between reporting-adjusted and unadjusted cancer incidence rates for the 1998 diagnosis year ranged from 3% for colorectal cancers to 14% for melanoma in whites and for prostate cancer in black males. Reporting-adjusted current incidence trends for breast cancer and lung/bronchus cancer in white females showed statistically significant increases (estimated annual percent change [EAPC] = 0.6%, 95% confidence interval [CI] = 0.1% to 1.2%) and 1.2%, 95% CI = 0.7% to 1.6%, respectively), whereas trends for these cancers using unadjusted incidence rates were not statistically significantly different from zero (EAPC = 0.4%, 95% CI = -0.1% to 0.9% and 0.5%, 95% CI = -0.1% to 1.1%, respectively). Reporting-adjusted melanoma incidence rates for white males showed a statistically significant increase since 1981 (EAPC = 4.1%, 95% CI = 3.8% to 4.4%) in contrast to the unadjusted incidence rate, which was most consistent with a flat or downward trend (EAPC = -4.2%, 95% CI = -11.1% to 3.3%) after 1996. CONCLUSIONS: Reporting-adjusted cancer incidence rates are valuable in precisely determining current cancer incidence rates and trends and in monitoring the timeliness of data collection. Ignoring reporting delay and reporting error may produce downwardly biased cancer incidence trends, particularly in the most recent diagnosis years.

Cancer survival among US whites and minorities: a SEER (Surveillance, Epidemiology, and End Results) Program population-based study.

BACKGROUND: Available cancer statistics pertain primarily to white and African American populations. This study describes racial or ethnic patterns of cancer-specific survival and relative risks (RRs) of cancer death for all cancers combined and for cancers of the colon and rectum, lung and bronchus, prostate, and female breast for the 6 major US racial or ethnic groups. METHODS: Cancer-specific survival rates were analyzed for more than 1.78 million patients who resided in the 9 SEER (Surveillance, Epidemiology, and End Results) Program geographic areas and were diagnosed between 1975 and 1997 as having an incident invasive cancer, by 6 racial or ethnic groups (non-Hispanic whites, Hispanic whites, African Americans, Asian Americans, Hawaiian natives, and American Indians and Alaskan natives). RESULTS: Survival rates improved between 1988 to 1997 for virtually all racial or ethnic groups. However, racial or ethnic differences in RRs of cancer death persisted after controlling for age for all cancers combined and for age and stage for specific cancer sites (P<.01). African American, American Indian and Alaskan native, and Hawaiian native patients tended to have higher RRs of cancer death than the other groups. American Indians and Alaskan natives generally exhibited the highest RRs of cancer death, except for colorectal cancer in males. CONCLUSIONS: Survival rates in patients with cancer have improved in recent years, but racial or ethnic differences in survival rates and in RRs of cancer death persist. Additional studies are needed to clarify the socioeconomic, medical, biological, cultural, and other determinants of these findings.

Changing area socioeconomic patterns in U.S. cancer mortality, 1950-1998: Part I--All cancers among men.

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