Showing papers by "Eric J. Feuer published in 2005"

Cancer statistics, 2005.

Abstract: Each year, the American Cancer Society estimates the number of new cancer cases and deaths expected in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival based on incidence data from the National Cancer Institute and mortality data from the National Center for Health Statistics. Incidence and death rates are age-standardized to the 2000 US standard million population. A total of 1,372,910 new cancer cases and 570,280 deaths are expected in the United States in 2005. When deaths are aggregated by age, cancer has surpassed heart disease as the leading cause of death for persons younger than 85 since 1999. When adjusted to delayed reporting, cancer incidence rates stabilized in men from 1995 through 2001 but continued to increase by 0.3% per year from 1987 through 2001 in women. The death rate from all cancers combined has decreased by 1.5% per year since 1993 among men and by 0.8% per year since 1992 among women. The mortality rate has also continued to decrease from the three most common cancer sites in men (lung and bronchus, colon and rectum, and prostate) and from breast and colorectal cancers in women. Lung cancer mortality among women has leveled off after increasing for many decades. In analyses by race and ethnicity, African American men and women have 40% and 20% higher death rates from all cancers combined than White men and women, respectively. Cancer incidence and death rates are lower in other racial and ethnic groups than in Whites and African Americans for all sites combined and for the four major cancer sites. However, these groups generally have higher rates for stomach, liver, and cervical cancers than Whites. Furthermore, minority populations are more likely to be diagnosed with advanced stage disease than are Whites. Progress in reducing the burden of suffering and death from cancer can be accelerated by applying existing cancer control knowledge across all segments of the population.

Effect of screening and adjuvant therapy on mortality from breast cancer

Abstract: BACKGROUND We used modeling techniques to assess the relative and absolute contributions of screening mammography and adjuvant treatment to the reduction in breast-cancer mortality in the United States from 1975 to 2000. METHODS A consortium of investigators developed seven independent statistical models of breast-cancer incidence and mortality. All seven groups used the same sources to obtain data on the use of screening mammography, adjuvant treatment, and benefits of treatment with respect to the rate of death from breast cancer. RESULTS The proportion of the total reduction in the rate of death from breast cancer attributed to screening varied in the seven models from 28 to 65 percent (median, 46 percent), with adjuvant treatment contributing the rest. The variability across models in the absolute contribution of screening was larger than it was for treatment, reflecting the greater uncertainty associated with estimating the benefit of screening. CONCLUSIONS Seven statistical models showed that both screening mammography and treatment have helped reduce the rate of death from breast cancer in the United States.

Bayesian model selection for join point regression with application to age-adjusted cancer rates

Abstract: tively, the posterior distributions of the parameters and competing models Mk are computed by Markov chain Monte Carlo simulations. The Bayes information criterion BIC is used to select the model Mk with the smallest value of BIC as the best model. Another approach based on the Bayes factor selects the model Mk with the largest posterior probability as the best model when the prior distribution of Mk is discrete uniform. Both methods are applied to analyse the observed US cancer incidence rates for some selected cancer sites. The graphs of the join point models fitted to the data are produced by using the methods proposed and compared with the method of Kim and co-workers that is based on a series of permutation tests. The analyses show that the Bayes factor is sensitive to the prior specification of the variance U2, and that the model which is selected by BIC fits the data as well as the model that is selected by the permutation test and has the advantage of producing the posterior distribution for the join points. The Bayesian join point model and model selection method that are presented here will be integrated in the National Cancer Institute's join point software (http://www.srab.cancer.gov/joinpoint/) and will be available to the public.

Modeling the dissemination of mammography in the United States.

Abstract: 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.

Modeling Reporting Delays and Reporting Corrections in Cancer Registry Data

Abstract: The Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute is an authoritative source of cancer incidence statistics in the United States. The SEER program is a consortium of population-based cancer registries from different areas of the country. Each registry is charged with collecting data on all cancers that occur within its geographic area. As with any disease registry, there is a delay between the time that the disease (cancer) is first diagnosed and the time that it is reported to the registry. The SEER program has allowed for reporting delays of up to 19-months before releasing data for public use. Nevertheless, additional cases are discovered after the 19-month delay, and these cases are added in subsequent releases of the data. Further, any errors discovered are corrected in subsequent releases. Such reporting delays and corrections typically lead to underestimation of the cancer incidence rates in recent diagnosis years, making it difficult to monitor trends....

Geographic association between mammography use and mortality reduction in the US

Abstract: Background: Breast cancer mortality rates in women have been declining at the same time as breast cancer incidence rates, mammography rates and use of effective adjuvant therapy have been increasing. The objective of this study was to examine population data on breast cancer screening and breast cancer mortality to see if there is any geographic association between mammographic screening and breast cancer mortality reduction in the US, after adjusting for therapy use. Methods: Regression analysis of the estimated annual percent reduction of breast cancer mortality was performed on mammography use from the Behavioral Risk Factors Surveillance System (BRFSS) at state level. A secondary regression analysis on the SEER-11 region, at an aggregated Health Services Area (HSA) level, was carried out to adjust for use of adjuvant therapy. The annual percent change in the incidence of early stage cancer, calculated from SEER cancer data was used as a surrogate for mammography use. Adjuvant therapy use was estimated from SEER data and adjusted using the Patterns of Care data. Results: All the analyses showed a small but significant negative correlation between mammography usage and mortality reduction (correlation of −0.285, p-value 0.045) in breast cancer (state level) and change in ‘early’ stage breast cancer and mortality reduction (at HSA level) unadjusted (correlation of −0.307, p-value 0.065) and adjusted (partial correlation of −0.337, p-value 0.044) for adjuvant therapy use. Discussion: The results of the two analyses appear to show a moderate effect of mammography usage on decreasing breast cancer mortality in the US, which seems to support the conclusions of randomized mammographic screening trials. While randomized controlled trials are certainly the gold standard in appraising the efficacy of new screening or treatment modalities, such trials are conducted under standardized conditions and do not always reflect the effect of these interventions at population level. This paper attempts to examine population level effects through ecologic analyses. Results, however, need to be interpreted cautiously owing to the limitations and biases inherent in such analyses.