Breast Cancer Mortality Data Research Articles

The University of Wisconsin Breast Cancer Epidemiology Simulation Model: An Update.

The University of Wisconsin Breast Cancer Epidemiology Simulation Model (UWBCS), also referred to as Model W, is a discrete-event microsimulation model that uses a systems engineering approach to replicate breast cancer epidemiology in the US over time. This population-based model simulates the lifetimes of individual women through 4 main model components: breast cancer natural history, detection, treatment, and mortality. A key feature of the UWBCS is that, in addition to specifying a population distribution in tumor growth rates, the model allows for heterogeneity in tumor behavior, with some tumors having limited malignant potential (i.e., would never become fatal in a woman's lifetime if left untreated) and some tumors being very aggressive based on metastatic spread early in their onset. The model is calibrated to Surveillance, Epidemiology, and End Results (SEER) breast cancer incidence and mortality data from 1975 to 2010, and cross-validated against data from the Wisconsin cancer reporting system. The UWBCS model generates detailed outputs including underlying disease states and observed clinical outcomes by age and calendar year, as well as costs, resource usage, and quality of life associated with screening and treatment. The UWBCS has been recently updated to account for differences in breast cancer detection, treatment, and survival by molecular subtypes (defined by ER/HER2 status), to reflect the recent advances in screening and treatment, and to consider a range of breast cancer risk factors, including breast density, race, body-mass-index, and the use of postmenopausal hormone therapy. Therefore, the model can evaluate novel screening strategies, such as risk-based screening, and can assess breast cancer outcomes by breast cancer molecular subtype. In this article, we describe the most up-to-date version of the UWBCS.

Assessment of spatial variation in breast cancer-specific mortality using Louisiana SEER data

BackgroundPrevious studies suggest spatial differences in mortality for many types of cancer, including breast cancer. Identifying explanations for these spatial differences results in a better understanding of what leads to longer survival time. MethodsWe used a Bayesian accelerated failure time model with spatial frailty terms to investigate potential spatial differences in breast cancer mortality following breast cancer diagnosis using 2000–2013 Louisiana SEER data. ResultsThere are meaningful spatial differences in breast cancer mortality across the parishes of Louisiana, even after adjusting for known demographic and clinical risk factors. For example, the average survival time of a woman diagnosed in Orleans parish was 1.51 times longer than that of a woman diagnosed in Terrebonne parish. Additionally, there is evidence to suggest shorter survival times in lower income parishes along the Red and Mississippi Rivers, as well as parishes with lower socioeconomic status, less access to care and fresh food, worse quality of care, and more workers in certain industries. ConclusionThe addition of spatial frailties to account for an individual's geographic location is useful when analyzing breast cancer mortality data. Our findings suggest that survival following breast cancer diagnosis could potentially be improved if socioeconomic status differences were addressed, healthcare improved in quality and became more accessible, and certain industrial situations were improved for individuals diagnosed in parishes identified as having shorter average survival times.

Flexible Bayesian P-splines for smoothing age-specific spatio-temporal mortality patterns.

In this paper age-space-time models based on one and two-dimensional P-splines with B-spline bases are proposed for smoothing mortality rates, where both fixed relative scale and scale invariant two-dimensional penalties are examined. Model fitting and inference are carried out using integrated nested Laplace approximations, a recent Bayesian technique that speeds up computations compared to McMC methods. The models will be illustrated with Spanish breast cancer mortality data during the period 1985-2010, where a general decline in breast cancer mortality has been observed in Spanish provinces in the last decades. The results reveal that mortality rates for the oldest age groups do not decrease in all provinces.

Assessing predicted age-specific breast cancer mortality rates in 27 European countries by 2020.

We assessed differences in predicted breast cancer (BC) mortality rates, across Europe, by 2020, taking into account changes in the time trends of BC mortality rates during the period 2000-2010. BC mortality data, for 27 European Union (EU) countries, were extracted from the World Health Organization mortality database. First, we compared BC mortality data between time periods 2000-2004 and 2006-2010 through standardized mortality ratios (SMRs) and carrying out a graphical assessment of the age-specific rates. Second, making use of the base period 2006-2012, we predicted BC mortality rates by 2020. Finally, making use of the SMRs and the predicted data, we identified a clustering of countries, assessing differences in the time trends between the areas defined in this clustering. The clustering approach identified two clusters of countries: the first cluster were countries where BC predicted mortality rates, in 2020, might slightly increase among women aged 69 and older compared with 2010 [Greece (SMR 1.01), Croatia (SMR 1.02), Latvia (SMR 1.15), Poland (SMR 1.14), Estonia (SMR 1.16), Bulgaria (SMR 1.13), Lithuania (SMR 1.03), Romania (SMR 1.13) and Slovakia (SMR 1.06)]. The second cluster was those countries where BC mortality rates level off or decrease in all age groups (remaining countries). However, BC mortality rates between these clusters might diminish and converge to similar figures by 2020. For the year 2020, our predictions have shown a converging pattern of BC mortality rates between European regions. Reducing disparities, in access to screening and treatment, could have a substantial effect in countries where a non-decreasing trend in age-specific BC mortality rates has been predicted.

Impact of extending screening mammography to older women: Information to support informed choices.

From 2013 through 2017, the Australian national breast cancer screening programme is gradually inviting women aged 70-74 years to attend screening, following a policy decision to extend invitations to older women. We estimate the benefits and harms of the new package of biennial screening from age 50-74 compared with the previous programme of screening from age 50-69. Using a Markov model, we applied estimates of the relative risk reduction for breast cancer mortality and the risk of overdiagnosis from the Independent UK Panel on Breast Cancer Screening review to Australian breast cancer incidence and mortality data. We estimated screening specific outcomes (recalls for further imaging, biopsies, false positives, and interval cancer rates) from data published by BreastScreen Australia. When compared with stopping at age 69, screening 1,000 women to age 74 is likely to avert one more breast cancer death, with an additional 78 women receiving a false positive result and another 28 women diagnosed with breast cancer, of whom eight will be overdiagnosed and overtreated. The extra 5 years of screening results in approximately 7 more overdiagnosed cancers to avert one more breast cancer death. Thus extending screening mammography in Australia to older women results in a less favourable harm to benefit ratio than stopping at age 69. Supporting informed decision making for this age group should be a public health priority.

In spatio-temporal disease mapping models, identifiability constraints affect PQL and INLA results

Disease mapping studies the distribution of relative risks or rates in space and time, and typically relies on generalized linear mixed models (GLMMs) including fixed effects and spatial, temporal, and spatio-temporal random effects. These GLMMs are typically not identifiable and constraints are required to achieve sensible results. However, automatic specification of constraints can sometimes lead to misleading results. In particular, the penalized quasi-likelihood fitting technique automatically centers the random effects even when this is not necessary. In the Bayesian approach, the recently-introduced integrated nested Laplace approximations computing technique can also produce wrong results if constraints are not well-specified. In this paper the spatial, temporal, and spatio-temporal interaction random effects are reparameterized using the spectral decompositions of their precision matrices to establish the appropriate identifiability constraints. Breast cancer mortality data from Spain is used to illustrate the ideas.

Female Breast Cancer Mortality Clusters in Shandong Province, China: A Spatial Analysis

This study aimed to detect the spatial distribution and high-risk clusters of female breast cancer mortality for the years 2011 to 2013 in Shandong Province, China. The urban-rural difference in the spatial distribution and clusters of disease mortality were also examined. Breast cancer mortality data were obtained from the Shandong Death Registration System (SDRS) during 2011 to 2013 and were adjusted for the underreporting rate. The purely spatial scan Statistics method was performed using Discrete Poisson model. Seven significant spatial clusters for high mortality of female breast cancer were detected in Shandong Province at the county level; these clusters were mainly located in the eastern, southern, southwestern, central and northern regions. The spatial distributions differed significantly between urban and rural populations. Population ageing influenced the distribution of breast cancer clusters for the urban eastern residents. This study provided evidence for the presence of clusters of breast cancer mortality in Shandong, China and found urban-rural difference in the clusters, which is helpful for developing effective strategies to control breast cancer in different areas.

Black:white disparities in breast cancer mortality in the 50 largest cities in the United States, 2005–2014

IntroductionThis paper presents race-specific breast cancer mortality rates and the corresponding rate ratios for the 50 largest U.S. cities for each of the 5-year intervals between 2005 and 2014. MethodsThe 50 largest cities in the U.S. were the units of analysis. Numerator data were abstracted from national death files where the cause was malignant neoplasm of the breast (ICD-10=C50) for women. Population-based denominators were obtained from the U.S. Census Bureau for 2010–2014. To measure the racial disparity, we calculated Black:White rate ratios (RRs) and confidence intervals for each 5-year period. To determine whether changes over time in the disparity were statistically significant, we calculated a 2-sided z score for the change in the relative percent difference between the Black and White rates for 2005–2009 and 2010–2014. ResultsAt the most recent time point (2010–2014), the RR was significantly greater than 1.00 in the US and 24 cities. The change in the Black:White disparity was statistically significant in five cities and the US. The percent difference increased significantly in Atlanta, GA (from 4.1 to 117.4, p<0.001); San Antonio, TX (from 24.4 to 79.3, p=0.034); and the US (from 39.7 to 43.1, p=0.007). The percent difference decreased significantly in Memphis, TN (from 111.0 to 68.9, p=0.043); Philadelphia, PA (from 43.1 to 23.5, p=0.049); and Boston, MA (from 48.9 to 0.7, p=0.022). ConclusionThis analysis provides updated city-level breast cancer mortality data for Black and White women through 2014, and reveals that in the US and 24 of the 43 largest US cities, Black women continue to die from breast cancer at a higher rate than their White counterparts. Importantly, however, a few cities, Memphis, Boston and Philadelphia, showed a decrease in the Black:White breast cancer mortality disparity between 2005–2009 and 2010–2014.

Temporal Trends in Geographical Variation in Breast Cancer Mortality in China, 1973-2005: An Analysis of Nationwide Surveys on Cause of Death.

To describe geographical variation in breast cancer mortality over time, we analysed breast cancer mortality data from three retrospective national surveys on causes of death in recent decades in China. We first calculated the age-standardized mortality rate (ASMR) for each of the 31 provinces in mainland China stratified by survey period (1973–1975, 1990–1992 and 2004–2005). To test whether the geographical variation in breast cancer mortality changed over time, we then estimated the rate ratio (RR) for the aggregated data for seven regions and three economic zones using generalized linear models. Finally, we examined the correlation between mortality rate and several macro-economic measures at the provincial level. We found that the overall ASMR increased from 2.98 per 100,000 in 1973–1975 to 3.08 per 100,000 in 1990–1992, and to 3.85 per 100,000 in 2004–2005. Geographical variation in breast cancer mortality also increased significantly over time at the regional level (p = 0.002) but not at the economic zone (p = 0.089) level, with RR being generally lower for Western China (Northwest and Southwest) and higher in Northeast China over the three survey periods. These temporal and spatial trends in breast cancer mortality were found to be correlated with per capita gross domestic product, number of hospitals and health centres’ beds per 10,000 population and number of practicing doctors per 10,000 population, and average number of live births for women aged 15–64. It may be necessary to target public health policies in China to address the widening geographic variation in breast cancer mortality, and to take steps to ensure that the ease of access and the quality of cancer care across the country is improved for all residents.

Management of Ductal Carcinoma In Situ of the Breast: A Review.

Ductal carcinoma in situ (DCIS) of the breast represents a disease process that continues to increase in incidence with treatment paradigms that continue to evolve. Greater access to long-term data from large observational studies addressing the natural history of the disease has contributed to changes in treatment paradigms and put into question traditional management strategies. While recent analyses have suggested that a more conservative approach to the management of DCIS without surgical intervention or radiation therapy may be advisable based on breast cancer mortality data, there is a lack of level 1 or prospective evidence to support the widespread adoption of these approaches. Currently, surgery remains the standard of care for the initial treatment of DCIS. Adjuvant radiation therapy (RT) has consistently demonstrated a reduction in the risk of local recurrence following breast-conserving surgery (BCS), even in "low-risk" populations of patients. Invasive recurrences following BCS are associated with increases in breast cancer mortality. Questions that remain to be answered include (1) what constitutes an acceptable risk of local recurrence, (2) what are the costs associated with managing local recurrences compared with RT given initially after BCS (particularly in light of data supporting shorter courses of RT), and (3) what are the benefits of endocrine therapy on local recurrence, and do they justify the additional toxic effects and potential noncompliance with their long-term administration? Surgery and RT remain standard of care treatment options in the management of DCIS. Future studies are required to identify cohorts of patients in which RT can be safely omitted as well as to evaluate whether short-course RT alone may represent a better option than endocrine therapy with respect to compliance, toxic effects, cost and local control following BCS.

Affluence and Breast Cancer.

High income, high socioeconomic status, and affluence increase breast cancer incidence. Socioeconomic status in USA breast cancer studies has been assessed by block-group socioeconomic measures. A block group is a portion of a census tract with boundaries that segregate, as far as possible, socioeconomic groups. In this study, we used US Census income data instead of block groups to gauge socioeconomic status of breast cancer patients in relationship with incidence, prognostic markers, and survival. US state breast cancer incidence and mortality data are from the U.S. Cancer Statistics Working Group, United States Cancer Statistics: 1999-2011. Three-Year-Average Median Household Income by State, 2010 to 2012, is from the U.S. Census Bureau, Current Population Survey, 2011 to 2013 Annual Social and Economic Supplements. County incomes are from the 2005-2009 American Community Survey of the U.S. Census Bureau. The American Community Survey is an ongoing statistical survey that samples a small percentage of the population yearly. Its purpose is to provide communities the information they need to plan investments and services. Breast cancer county incidence and survival data are from the National Cancer Institute's Surveillance, Epidemiology and End Results Program (SEER) data base. We analyzed SEER data from 198 counties in California, Connecticut, Georgia, Hawaii, Iowa, New Mexico, Utah, and Washington. SEER uses the Collaborative Stage (CS) Data Collection System. We have retained the SEER CS variables. There was a significant relationship of income with breast cancer incidence in 50 USA states and the District of Columbia in White women (r = 0.623, p < 0.001). There was a significant relationship between node involvement and income in Whites in 198 USA counties. Income was significantly correlated with 5-year relative survival in Whites with localized breast cancer. Income was not correlated with 5-year survival of Black race (p = 0.364) or other races (p = 0.624). The multivariate general linear model with income as covariate, 5-year survival by race as a dependent variable, showed a significant effect of income and White race on 5-year survival (p < 0.001), unrelated to Black race (p = 0.780) or other races (p = 0.618). In men, we found a nonsignificant positive correlation between county breast cancer incidence and income (r = 0.098, p = 0.168). Breast cancer risk factors, such as delayed childbirth, less breast-feeding, and use of hormone supplements, are more common in affluent women. Affluent women are more likely to have mammograms, which detect many cancers that might not otherwise be diagnosed. In addition, women in certain affluent ethnic groups-Ashkenazi Jews, Icelanders and the Dutch-are more likely to carry genetic mutations known to predispose to breast cancer. We hypothesize that women with more income can afford better cancer care and survive longer than poorer women. But our hypothesis does not explain why this effect should be limited to White women; or why node involvement increased with income in White women but not in Blacks or Hispanics. Further studies may be worthwhile.

PCN299 - The Clinical and Economic Burden of Skeletal Related Events in Austria, Czech Republic, Germany, Greece, Italy, Spain and Switzerland: A Comparison Between the use of Denosumab and Zoledronic acid in Patients With Breast Cancer and Bone Metastases

To estimate the total number of skeletal-related events (SREs) prevented, related hospital burden and direct medical expenditure when denosumab is used instead of zoledronic acid for SRE prevention in seven European countries. A model was developed combining country-specific breast cancer mortality data from the World Health Organization or from local registries, the proportion of metastatic breast cancer patients with bone metastases (BMs) at high risk of developing SREs from published literature, the prevalence of breast cancer with BMs was derived assuming a steady state approach (with incidence equivalent to mortality of metastatic disease), and the average survival for metastatic breast cancer patients from published literature. Annual SRE rates from a European observational retrospective study and the treatment effect observed in a head-to-head clinical trial of denosumab versus zoledronic acid were used to estimate the total number of predicted SREs by treatment option. Country-specific findings from two retrospective studies were used to estimate the total number of hospitalisations, inpatient days and direct medical costs associated with the management of SREs. Across all countries, the estimated prevalence of breast cancer patients with BMs was 87,592 patients: Austria, 3,253; Czech Republic, 3,509; Germany, 37,895; Greece, 4,639; Italy, 27,767; Spain, 7,553; and Switzerland, 2,975. When denosumab was the selected treatment option, 36,324 SREs were estimated to be avoided per year compared with the use of zoledronic acid, which would save 24,110 hospitalisations, 536,551 inpatient days and 206,605,754€ of direct medical costs (2014 costs in €: Austria, 23,890,554; Czech Republic, 5,779,856; Germany, 60,659,628; Greece, 15,941,728; Italy, 39,998,970; Spain, 17,249,548; and Switzerland, 43,085,469). Denosumab’s clinical superiority in the prevention of SREs over zoledronic acid could result in significant savings in the number and duration of hospitalisations, reducing direct medical costs associated with the management of SREs in Europe.

40 years of progress in female cancer death risk: a Bayesian spatio-temporal mapping analysis in Switzerland.

BackgroundIn the past decades, mortality of female gender related cancers declined in Switzerland and other developed countries. Differences in the decrease and in spatial patterns within Switzerland have been reported according to urbanisation and language region, and remain controversial. We aimed to investigate geographical and temporal trends of breast, ovarian, cervical and uterine cancer mortality, assess whether differential trends exist and to provide updated results until 2011.MethodsBreast, ovarian, cervical and uterine cancer mortality and population data for Switzerland in the period 1969–2011 was retrieved from the Swiss Federal Statistical office (FSO). Cases were grouped into <55 year olds, 55–74 year olds and 75+ year olds. The geographical unit of analysis was the municipality.To explore age- specific spatio-temporal patterns we fitted Bayesian hierarchical spatio-temporal models on subgroup-specific death rates indirectly standardized by national references. We used linguistic region and degree of urbanisation as covariates.ResultsFemale cancer mortality continuously decreased in terms of rates in all age groups and cancer sites except for ovarian cancer in 75+ year olds, especially since 1990 onwards.Contrary to other reports, we found no systematic difference between language regions. Urbanisation as a proxy for access to and quality of medical services, education and health consciousness seemed to have no influence on cancer mortality with the exception of uterine and ovarian cancer in specific age groups. We observed no obvious spatial pattern of mortality common for all cancer sites.Rate reduction in cervical cancer was even stronger than for other cancer sites.ConclusionsFemale gender related cancer mortality is continuously decreasing in Switzerland since 1990. Geographical differences are small, present on a regional or canton-overspanning level, and different for each cancer site and age group. No general significant association with cantonal or language region borders could be observed.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-015-1660-8) contains supplementary material, which is available to authorized users.

Checking unimodality using isotonic regression: an application to breast cancer mortality rates

In some diseases it is well-known that a unimodal mortality pattern exists. A clear example in developed countries is breast cancer, where mortality increased sharply until the nineties and then decreased. This clear unimodal pattern is not necessarily applicable to all regions within a country. In this paper, we develop statistical tools to check if the unimodality pattern persists within regions using order restricted inference. Break points as well as confidence intervals are also provided. In addition, a new test for checking monotonicity against unimodality is derived allowing to discriminate between a simple increasing pattern and an up-then-down response pattern. A comparison with the widely used joinpoint regression technique under unimodality is provided. We show that the joinpoint technique could fail when the underlying function is not piecewise linear. Results will be illustrated using age-specific breast cancer mortality data from Spain in the period 1975–2005.

Abstract P1-11-08: The impact of social inequalities on breast cancer mortality in Brazil

Abstract Introduction: Female breast cancer mortality has decreased considerably in developed nations. In contrast, an increase has been observed in developing countries. Objective: To describe the impact of social inequalities in female breast cancer mortality in Brazil, between the years of 1990 and 2011. Methods: Breast cancer mortality data and estimates for the resident population were obtained from the Brazilian National Health Service (SUS) database for the 1990-2011 period. Age-standardized mortality rates were calculated (20-39, 40-49, 50-69 and ≥70 years) by direct standardization using the 1960 standard world population. Trends were modeled using linear regression, with mortality rates as the dependent variable and the year of death as the independent variable. The Social Exclusion Index (SEI) and the Human Development Index (HDI) were used to classify the 27 Brazilian states. Pearson’s correlation was used to describe the association between the SEI and the HDI and the variations in mortality rates in each state. Results: Age-standardized mortality rates in Brazil were found to be stable (annual percent change [APC] = 0.32; 95%CI: -0.1 – 0.7). Statistically significant decreases in mortality rates were found in the states of Rio Grande do Sul, Rio de Janeiro and São Paulo. Increases in mortality rates were most notable in the states of Maranhão (APC = 11.2; 95%CI: 5.8 – 16.9), Piauí (APC = 9.8; 95%CI: 7.6 – 12.1) and Paraíba (APC = 9.3; 95%CI: 6.0 – 12.8). There was a statistically significant correlation between SEI and a change in female breast cancer mortality rates in the Brazilian states between 1990 and 2011 and between HDI and mortality between 2001 and 2011. This reduction was most notable in the Brazilian states with better socioeconomic conditions. Conclusions: It was observed a direct impact of social inequalities in female breast cancer mortality rates in Brazil. Reductions in these rates were found in the more developed states, possibly reflecting a better local healthcare. Citation Format: Ruffo Freitas-Junior, Carolina M Gonzaga, Maria-Paula Curado, Ana-Luiza L Sousa, Marta R Souza. The impact of social inequalities on breast cancer mortality in Brazil [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P1-11-08.

Temporal trends in female breast cancer mortality in Brazil and correlations with social inequalities: ecological time-series study.

BackgroundBreast cancer is the most common cause of death from cancer in women in less developed regions. Therefore, the objective of this study was to provide data on the temporal trends in female breast cancer mortality between 1990 and 2011 and to evaluate its association with the social inequalities present in Brazil.MethodsBreast cancer mortality data and estimates for the resident population were obtained from the Brazilian National Health Service database for the 1990–2011 period. Age-standardized mortality rates were calculated (20–39, 40–49, 50–69 and ≥70 years) by direct standardization using the 1960 standard world population. Trends were modeled using joinpoint regression model and linear regression. The Social Exclusion Index and the Human Development Index were used to classify the 27 Brazilian states. Pearson’s correlation was used to describe the association between the Social Exclusion Index and the Human DeveIopment and the variations in mortality rates in each state.ResultsAge-standardized mortality rates in Brazil were found to be stable (annual percent change [APC] = 0.3; 95% CI: −0.1 – 0.7) between 1994 and 2011. Considering the Brazilian states, significant decreases in mortality rates were found in Rio Grande do Sul, Rio de Janeiro and São Paulo. Increases in mortality rates were most notable in the states of Maranhão (APC = 11.2; 95 %CI: 5.8 – 16.9), Piauí (APC = 9.8; 95% CI: 7.6 – 12.1) and Paraíba (APC = 9.3; 95% CI: 6.0 – 12.8). There was a statistically significant correlation between Social Exclusion Index and a change in female breast cancer mortality rates in the Brazilian states between 1990 and 2011 and between Human Development Index and mortality between 2001 and 2011.ConclusionsFemale breast cancer mortality rates are stable in Brazil. Reductions in these rates were found in the more developed states, possibly reflecting better healthcare.

Insights from the breast cancer screening trials: how screening affects the natural history of breast cancer and implications for evaluating service screening programs.

It is desirable to have a strategy for evaluation of breast cancer service screening programs years before the long-term breast cancer mortality data are available. Since successful mammography screening has a significant impact on two components of the TNM (tumor size, node status, presence or absence of distant metastases) classification system, tumor size and node status, we investigated the effect of the randomized breast screening trials on incidence of advanced stage disease and on the subsequent breast cancer death rate. In the trials that achieved a 20% or greater reduction in advanced stage disease, there was an average breast cancer mortality reduction of 28% among women invited to screening (attenders and nonattenders combined). In the trials that achieved a reduction in advanced stage disease of less than 10%, there was no reduction in breast cancer mortality among women invited to screening. This study provides evidence that the average mortality reduction in all the trials underestimates the true mortality reduction, and that substantially greater breast cancer mortality reductions can be expected in screening programs that are effective in reducing advanced stage breast cancer. In addition, monitoring the incidence of advanced stage breast cancer in an ongoing screening program can provide a sensitive and early indicator of the subsequent mortality from the disease.

Influence of diabetes mellitus on mortality in breast cancer patients.

Breast cancer is one of the most common malignant tumours among women worldwide. Besides, diabetes mellitus is also a major health problem in developed countries. This study explores the association between diabetes mellitus and breast cancer patients' survival outcomes. A systematic literature search in Embase (http://www.embase.com) and MEDLINE (http://www.ncbi.nlm.nih.gov/pubmed) was conducted from January 1960 to April 2014 and systematically identified clinical studies that evaluated the association between breast cancer mortality and diabetes mellitus. Clinical studies investigating the association between diabetes mellitus and breast cancer patients' survival outcomes were included. Twenty publications were chosen for the meta-analysis, of which 16 studies had all-cause mortality data and 12 studies had breast cancer mortality data. Published from 2001 to 2013, all 20 studies followed a total of 2,645,249 patients including more than 207,832 diabetic patients. Pre-existing diabetes mellitus was associated with a 37% increased risk for all-cause mortality in women with breast cancer (hazard ratio (HR) = 1.37; 95% confidence interval (CI): 1.34-1.41; P = 0.02). Diabetes mellitus was associated with a 17% increased risk for breast cancer mortality in women with breast cancer (HR = 1.17; 95% CI: 1.11-1.22; P < 0.01). Women with diabetes mellitus are at higher risk of breast cancer-specific and all-cause mortality after initial breast cancer diagnosis.

Mortality of breast cancer in Taiwan, 1971–2010: Temporal changes and an age–period–cohort analysis

The current paper describes the age, period and cohort effects on breast cancer mortality in Taiwan. Female breast cancer mortality data were collected from the Taiwan death registries for 1971–2010. The annual percentage changes, age- standardised mortality rates (ASMR) and age–period–cohort model were calculated. The mortality rates increased with advancing age groups when fixing the period. The percentage change in the breast cancer mortality rate increased from 54.79% at aged 20–44 years, to 149.78% in those aged 45–64 years (between 1971–75 and 2006–10). The mortality rates in the 45–64 age group increased steadily from 1971 to 1975 and 2006–10. The 1951 birth cohorts (actual birth cohort; 1947–55) showed peak mortalities in both the 50–54 and 45–49 age groups. We found that the 1951 birth cohorts had the greatest mortality risk from breast cancer. This might be attributed to the DDT that was used in large amounts to prevent deaths from malaria in Taiwan. However, future researches require DDT data to evaluate the association between breast cancer and DDT use.

Female breast cancer statistics of 2010 in China: estimates based on data from 145 population-based cancer registries.

The aim of the study is to provide incidence and mortality data of female breast cancer at national level of China in 2010. A total of 145 population-based cancer registries submitted qualified cancer incidence and mortality data to National Cancer Registration Center of China. Based on the qualified cancer registries' data, we estimated the overall breast cancer incidence and mortality data of China in 2010 and reported breast cancer statistics by age and geographical area. The estimated number of female breast cancer cases was about 208 thousand. The crude incidence rate, age-standardized rate by China and World population were 32.43 per 100,000, 25.89 per 100,000 and 24.20 per 100,000, respectively. The incidence rates were higher in urban area than in rural area. And the incidence rates in Eastern area and Middle area were similar and higher that those in Western areas. The estimated number of female breast cancer death in 2010 of China was about 55.5 thousand. The crude, age-standardized mortalities by China population and World population were 8.65 per 100,000, 6.56 per 100,000 and 6.36 per 100,000, respectively. The mortality rates by geographical area had similar pattern to the incidence rates. Breast cancer is still a major health burden for Chinese women especially in urban area. Prevention strategies such as weight control, high-quality screening and diagnosis may help control the disease.