Karla Kerlikowske

Risks of untreated and treated isolated systolic hypertension in the elderly: meta-analysis of outcome trials

BACKGROUND Previous meta-analysis of outcome trials in hypertension have not specifically focused on isolated systolic hypertension or they have explained treatment benefit mainly in function of the achieved diastolic blood pressure reduction. We therefore undertook a quantitative overview of the trials to further evaluate the risks associated with systolic blood pressure in treated and untreated older patients with isolated systolic hypertension METHODS Patients were 60 years old or more. Systolic blood pressure was 160 mm Hg or greater and diastolic blood pressure was less than 95 mm Hg. We used non-parametric methods and Cox regression to model the risks associated with blood pressure and to correct for regression dilution bias. We calculated pooled effects of treatment from stratified 2 x 2 contingency tables after application of Zelens test of heterogeneity. FINDINGS In eight trials 15 693 patients with isolated systolic hypertension were followed up for 3.8 years (median). After correction for regression dilution bias, sex, age, and diastolic blood pressure, the relative hazard rates associated with a 10 mm Hg higher initial systolic blood pressure were 1.26 (p=0.0001) for total mortality, 1.22 (p=0.02) for stroke, but only 1.07 (p=0.37) for coronary events. Independent of systolic blood pressure, diastolic blood pressure was inversely correlated with total mortality, highlighting the role of pulse pressure as risk factor. Active treatment reduced total mortality by 13% (95% CI 2-22, p=0.02), cardiovascular mortality by 18%, all cardiovascular complications by 26%, stroke by 30%, and coronary events by 23%. The number of patients to treat for 5 years to prevent one major cardiovascular event was lower in men (18 vs 38), at or above age 70 (19 vs 39), and in patients with previous cardiovascular complications (16 vs 37). INTERPRETATION Drug treatment is justified in older patients with isolated systolic hypertension whose systolic blood pressure is 160 mm Hg or higher. Absolute benefit is larger in men, in patients aged 70 or more and in those with previous cardiovascular complications or wider pulse pressure. Treatment prevented stroke more effectively than coronary events. However, the absence of a relation between coronary events and systolic blood pressure in untreated patients suggests that the coronary protection may have been underestimated.

Individual and Combined Effects of Age, Breast Density, and Hormone Replacement Therapy Use on the Accuracy of Screening Mammography

Context High breast density increases breast cancer risk and the difficulty of reading mammograms. Breast density decreases with age and increases with postmenopausal hormone therapy use. The interplay of breast density, age, and hormone therapy use on the accuracy of mammography is uncertain. Contribution For women with fatty breasts, the sensitivity of mammography was 87% and the specificity was 96.9%. For women with extremely dense breasts, the sensitivity of mammography was 62.9% and the specificity was 89.1%. Sensitivity increased with age. Hormone therapy use was not an independent predictor of accuracy. Implications The accuracy of screening mammography is best in older women and in women with fatty breasts. Postmenopausal hormone therapy affects mammography accuracy only through its effects on breast density. The Editors Mammographic breast density may be the most undervalued and underused risk factor in studies investigating breast cancer occurrence (1). The risk for breast cancer is four to six times higher in women with dense breasts (2, 3). Breast density may also decrease the sensitivity and, thus, the accuracy of mammography. Radiographically dense breast tissue may obscure tumors, which increases the difficulty of detecting breast cancer. In addition, dense breast tissue may mimic breast cancer on mammography (4), which increases recall rates (4-12), reduces specificity, and compromises the benefit of screening in women with dense breasts (such as women who use HRT or who are premenopausal) (6, 8, 13). Breast density is affected by age, use of hormone replacement therapy (HRT), menstrual cycle phase, parity, body mass index, and familial or genetic tendency (4, 5, 14-21). Studies show that the sensitivity of mammography increases with age (6-8), especially in postmenopausal women whose breasts are less dense (8). Earlier research has examined the individual effect of each factor we have described, but most studies could not adequately examine the interaction of these factors because of insufficient sample size (4-15). Studies conducted in the 1970s with data from the Breast Cancer Detection Demonstration Project (22) and New York Health Insurance Plan (23) are based on mammographic examinations that are very different from those performed using current technology. The Mammography Quality Standards Act (24) and the standardized reporting efforts of the American College of Radiology (25) have resulted in important improvements in mammography that necessitate reexamination. We used data from the National Cancer Institutes Breast Cancer Surveillance Consortium (BCSC) (26) on 329 495 women in the United States who had 463 372 screening mammograms, which were linked to 2223 cases of breast cancer. Our goal was to examine the individual and combined effects of age, breast density, and HRT use on mammographic accuracy. This large data set provides a unique opportunity to examine these issues in women undergoing screening mammography in the United States, especially women younger than 50 years of age and older than 80 years of age. We chose to study a sample that had been recently screened (within the previous 2 years) so that the risk for breast cancer would be similar to that in women who receive routine mammographic screening. Methods Data Collection Initially, we included data on women 40 to 89 years of age who underwent screening mammography between 1996 and 1998, as submitted by seven registries in the BCSC (North Carolina; New Mexico; New Hampshire; Vermont; Colorado; Seattle, Washington; and San Francisco, California). We included women who reported having previous mammography or who had a previous mammographic examination recorded in a registry within 2 years of the index mammogram. Women with breast implants or a personal history of breast cancer were excluded. In addition, women with missing data for age (<1%), breast density (27%), or HRT use (21%) were excluded (36% of all data). Demographic characteristics, clinical characteristics, and accuracy measures for women missing any of this information were very similar to those for women with complete data. All registries obtained institutional review board approval for data collection and linkage procedures, and careful data management, processing, and security procedures were followed (27). Consortium mammography registries and data collection procedures are described elsewhere (26). Briefly, seven institutions in seven states receive funding from the National Cancer Institute to maintain mammography registries that cover complete or contiguous portions of each state. Data are collected similarly at each registry. Demographic and history information is collected from women at the time of mammography by using a self-administered survey or face-to-face interview methods. Variables include date of birth, history of previous mammography, race or ethnicity, current use of HRT (prescription medication used to treat perimenopausal and postmenopausal symptoms), and menopausal status. We assumed that women 55 years of age and older were perimenopausal or postmenopausal. For women 40 to 54 years of age, premenopausal status was defined as having regular menstrual periods with no HRT use; perimenopausal or postmenopausal status was defined as either removal of both ovaries or uncertainty about whether periods had stopped permanently. This latter category was further classified into HRT users and nonusers. These definitions recognize that HRT users with intact uteri may have menstrual-like bleeding. Additional data, including mammographic breast density, mammographic assessment, and recommended follow-up (based on the American College of Radiology Breast Imaging Reporting and Data System [BI-RADS]), are collected from the technologist and radiologist at the time of mammography (25). Pathology data are collected from one or more sources: regional Surveillance, Epidemiology, and End Results (SEER) programs, state cancer registries, or pathology laboratories. Design We included all screening examinations for women who met the described criteria and who had at least one screening mammogram in 1996, 1997, or 1998. These years were chosen to ensure 1-year follow-up for cancer reporting and to account for routine reporting schedules in obtaining data from SEER and state cancer registries. We classified mammography as screening if a radiologist indicated that the examination was a bilateral, two-view (craniocaudal and mediolateral) examination. To avoid including diagnostic examinations, we excluded any breast imaging study performed within the previous 9 months. Because our goal was to study routine screening, mammographic accuracy was calculated on the basis of the initial assessment of the screening views alone (only 6% required supplemental imaging). Interpretation codes included BI-RADS assessments of 0 (incomplete), 1 (negative), 2 (negative, benign), 3 (probably benign), 4 (suspicious abnormality), or 5 (highly suggestive of malignancy). In cases in which the initial screening visit included both a screening examination and additional imaging to determine an assessment, the initial screening assessment was assigned a 0 (incomplete assessment) for analysis. When a woman had different assessments by breast, we chose the highest-level assessment for the woman as a whole (woman-level assessment) on the basis of the following hierarchy of overall level of radiologic concern: 1 < 2 < 3 < 0 < 4 < 5. We defined a screening examination as positive if it was assigned a BI-RADS assessment code of 0, 4, or 5. An assessment code of 3 associated with a recommendation for immediate additional imaging, biopsy, or surgical evaluation was also classified as positive. Although the BI-RADS recommendation for a code 3 (probably benign) is short-interval follow-up, immediate work-up was recommended in 37% of code 3s in the pooled BCSC data; therefore, this assessment is more consistent with a BI-RADS code of 0 (incomplete assessment) (28). We defined a screening examination as negative if it received a BI-RADS assessment code of 1, 2, or 3 when associated with short-interval follow-up only or routine follow-up. We classified breast pathology outcomes as cancer if pathology or cancer registry data identified a diagnosis of invasive or ductal carcinoma in situ. Lobular carcinoma in situ (<0.01% of cancer cases in our pooled data) was not considered a diagnosis of cancer in our analyses because it cannot be detected by mammography and is not treated. Examinations were classified as false-positive when the assessment was positive and breast cancer was not diagnosed within the follow-up period (365 days after the index screening examination or until the next examination, whichever occurred first). Examinations were classified as true-positive when the assessment was positive and cancer was diagnosed. A false-negative examination was a negative assessment with a diagnosis of cancer within the follow-up period. A true-negative examination was a negative assessment with no subsequent diagnosis of cancer within the follow-up period. Radiographic breast density was defined according to BI-RADS as follows: 1) almost entirely fatty, 2) scattered fibroglandular tissue, 3) heterogeneously dense, and 4) extremely dense (25). We excluded one registry that collects two categories of breast density (dense or not dense) at some facilities. Statistical Analysis For age, breast density, and HRT groups, we calculated rates of incident breast cancer, rates of breast cancer detected by mammography, and rates of missed cancer. To examine the nonlinear effects of age, we categorized age into 10-year groups, except for ages 40 to 59, which were divided into 5-year groups to explore changes around menopause. Accuracy indices included sensitivity and specificity. Sensitivity was calculated as true-positive/(true-positive + false-negative). Specificity was calculated as true-negative/(true-negative + false

Using Clinical Factors and Mammographic Breast Density to Estimate Breast Cancer Risk: Development and Validation of a New Predictive Model

Context Existing breast cancer prediction tools do not account for breast density, a strong risk factor for breast cancer and have been studied in white women only. Contribution The authors developed a breast cancer risk prediction model that incorporates a measure of breast density routinely reported with mammography. Its predictions were accurate, but it had only modest ability to distinguish women who did not develop cancer from those who did, and it misclassified risk in some subgroups. Implication The model requires validation in additional populations. A breast cancer prediction model that incorporates breast density does well in some but not all domains of predicting risk. Its accuracy should be better characterized before it is used clinically. The Editors In 2007, breast cancer will have been diagnosed in more than 178000 women in the United States, and more than 40000 women will have died of breast cancer (1). Most of these women never had their risk for breast cancer assessed, and even fewer considered chemoprevention (25). Providing women with an estimate of their risk for breast cancer would provide an opportunity for them to consider options to decrease their risk. Women at low short-term risk for breast cancer may experience less anxiety about their health and would be less likely to benefit from prevention efforts. Women at very high risk may warrant additional screening tests, such as breast magnetic resonance imaging (6), and might benefit from chemoprevention of breast cancer with tamoxifen or raloxifene. The standard risk assessment model available to practitioners (the Gail model) (7) identifies only a minority of women who eventually develop breast cancer being at high risk (8). Better breast cancer risk prediction tools are needed (9). The radiographic appearance of the breast has been consistently shown to be a major risk factor for breast cancer, whether it is defined by a qualitative assessment of the parenchymal pattern or a quantitative measure of percentage of density (1012). Women in whom more than 50% of total breast area is mammographically dense have high breast density and are at 3- to 5-fold greater risk for breast cancer than women in whom breast density is less than 25% (10, 1316). The increased risk for breast cancer associated with breast density is due in part to the lower sensitivity of mammography in dense breasts (1719), but the association remains strong after accounting for masking (20, 21). Mammographically dense breast tissue is rich in epithelium and stroma (10), and the association could represent activation of epithelial cells or fibroblasts (2225). Recently, several models have been published that incorporate breast density: One uses a continuous measure of breast density that is not available to clinicians and has not been validated (26), and the other predicts 1-year risk for breast cancer (27). We previously demonstrated that a simple model based on age, ethnicity, and a categorical measure of breast density had predictive accuracy similar to that of the Gail model in a multiethnic cohort of women receiving screening mammograms in northern California (28). We expand on that work by using data from more than 1 million ethnically diverse women throughout the United States to develop and validate a risk assessment tool that incorporates breast density and therefore might improve breast cancer screening and prevention efforts. Methods Study Population We included 1095484 women age 35 years or older who had had at least 1 mammogram with breast density measured by using the Breast Imaging Reporting and Data System (BI-RADS) classification system in any of the 7 mammography registries participating in the National Cancer Institutefunded Breast Cancer Surveillance Consortium (BCSC) (available at breastscreening.cancer.gov) (29). The BCSC is a community-based, ethnically and geographically diverse sample that broadly represents the United States (30). We excluded women who had a diagnosis of breast cancer before their first eligible mammography examination. Because our goal was to develop a model of long-term risk for invasive breast cancer, we excluded women with cancer diagnosed in the first 6 months of follow-up to minimize the number of cases of cancer included in the model that were diagnosed on the basis of the mammogram used for risk assessment. Women were also excluded if they had breast implants. Women in whom ductal carcinoma in situ was diagnosed were censored at the time of diagnosis in the primary analysis. When women had several mammograms, we based our analysis on findings from the first mammogram. Each registry obtains annual approval from its institutional review board for consenting processes or a waiver of consent, enrollment of participants, and ongoing data linkage for research purposes. All registries have received a Certificate of Confidentiality from the federal government that protects the identities of research participants. Measurement of Risk Factors Patient information was obtained primarily from self-report at the time of mammography. We selected 2 risk factors in addition to breast density for inclusion in the model on the basis of simplicity (yes or no) and a high attributable risk: history of breast cancer in a first-degree relative and history of a breast biopsy. Body mass index was later considered for addition to the model, but it was excluded to maintain parsimony and because it had minimal effect on model discrimination (the increase in the concordance statistic [c-statistic] was only 0.003). For modeling and validation, missing data for relatives with breast cancer and number of breast biopsies were set to 0. The 5-year Gail risk was computed for each woman by using the algorithms provided by the National Cancer Institute to calculate the Gail model risk for individual women (31). For Gail model calculations, missing data were coded as specified by that model (age at menarche as14 years, age at first live birth as<20 years, number of breast biopsies as 0, and number of first-degree relatives as 0). Ethnicity was coded by using the expanded race and ethnicity definition currently used in the Surveillance, Epidemiology, and End Results (SEER) database and U.S. Vital Statistics (non-Hispanic White, non-Hispanic Black, Asian or Pacific Islander, Native American/Alaskan Native, Hispanic, or other). We classified women who self-identified as mixed or other race with participants who did not report race and ethnicity. Breast Density Community radiologists at each site classified breast density on screening mammograms as part of routine clinical practice by using the American College of Radiology BI-RADS density categories (32): almost entirely fat (category 1), scattered fibroglandular densities (category 2), heterogeneously dense (category 3), and extremely dense (category 4). The BI-RADS category 2 was used as the reference group for breast density because it formed the largest group. Ascertainment of Breast Cancer Cases Breast cancer outcomes (invasive cancer and ductal carcinoma in situ) were obtained at each site through linkage with the regional population-based SEER program, state tumor registries, and pathology databases. Vital Status Vital status was obtained through linkage to SEER registries, state tumor registries, and the individual state vital statistics or the National Death Index. Model Development We used a proportional hazards model of invasive breast cancer to estimate the hazard ratios for each BI-RADS breast density category. Women entered the model 6 months after the index mammogram and were censored at the time of death, diagnosis of ductal carcinoma in situ, or the end of follow-up. All models were adjusted for age (in 5-year intervals) and race and ethnicity. The strength of the breast density association with breast cancer was greater for women younger than age 65 years (P for interaction< 0.001). Thus, separate models were fitted for women younger than age 65 years and for women age 65 years or older. No other interaction terms were included in the final model. We calculated similar estimates for first-degree relatives with breast cancer (yes or no) and a personal history of breast biopsy (yes or no) from the BCSC. All predictors met the proportional hazards assumption that was assessed by loglog plots and by including interaction terms with time for each predictor variable. We then developed an absolute risk model by using methods described in the Appendix Figure. The model primarily estimates predicted incidence of invasive breast cancer by using age, race or ethnicity, and breast density. These estimates are then adjusted for family history and biopsy history if available. We based our estimates of breast cancer incidence on the SEER age- and ethnicity-specific risk for invasive breast cancer (1992 to 2002) (33). Age-specific incidence for each ethnic group was estimated by fitting a third-order polynomial model to the SEER data. Age-specific incidence rates for the Native American and Alaskan Native group were inconsistent in SEER, so we excluded this group from further analyses. We calculated the baseline risk for the model by adjusting SEER incidence for the populations attributable risk for each breast density subgroup. We estimated the age- and ethnicity-specific distribution of mammographic breast density needed for these calculations by using data from a larger set of 3343047 mammograms from the BCSC. The distribution of breast density varied statistically significantly by age and by race or ethnicity (P< 0.001 for each comparison). The model used these variations by age and race to distribute the 5-year risk for invasive breast cancer across the 4 breast density subgroups. We used the methods described by Gail and colleagues (7) to translate the hazard ratios and risk factor distributions into absolute risks. The age-, sex-, and ethnicity-specific competing risks for death for women were calcula

Changes in the use of Postmenopausal hormone therapy after the publication of clinical trial results

Context Since 1998, 2 large trials have drastically changed the evidence for the preventive health benefits of postmenopausal hormone replacement therapy. However, changes in practice often lag behind changes in evidence. Contribution Among mammography recipients in San Francisco, California, the use of hormone replacement therapy decreased 1% per quarter after publication of the Heart and Estrogen/progestin Replacement Study and 18% per quarter after publication of results from the Womens Health Initiative (WHI). Reduction in use was unrelated to a womans age, hysterectomy status, or race or ethnicity. Implications The WHI resulted in more dramatic changes in practice than are often associated with changes in evidence. The vigorous media coverage of the WHI may have contributed to rapid changes in practice. The Editors In 1995, approximately 38% of postmenopausal women in the United States were taking hormone therapy (1). At that time, several observational studies had suggested that hormone therapy offered women some protection against coronary heart disease and osteoporosis (2-5). A decision analysis published in 1997 concluded that the benefits of hormone therapy outweighed its risks for nearly all women (6). More recently, the results from 2 large randomized clinical trials, the Heart and Estrogen/progestin Replacement Study (HERS) (7) and the Womens Health Initiative (WHI), have been published (8). These clinical trials demonstrated that the risks associated with hormone therapy outweigh the benefits for women taking continuous estrogen and progestin regimens. As a result of these trial results, the U.S. Food and Drug Administration required new warning labels for all estrogen products (9), and the U.S. Preventive Services Task Force revised its assessment of hormone therapy to recommend against the routine use of estrogen and progestin for the prevention of chronic conditions in postmenopausal women (10). It is important to understand whether this new scientific evidence is changing the use of hormone therapy. Of note, the results of the WHI were widely disseminated. Despite this publicity, differential access to new information, varied interpretations of study findings, and individual perceptions of menopausal symptoms and hormone side effects may have resulted in different patterns of use. An understanding of how use is changing over time provides important information about the dissemination of clinical trial results to women. We designed our analysis to examine whether the use of hormone therapy has changed among postmenopausal women as a result of the publication of the results from HERS and the WHI. We were also interested in examining whether patterns of use differ by patient characteristics. Because HERS examined the outcomes of older women, we hypothesized that there would be earlier and more substantial declines in hormone therapy use among this group. We also expected that there would be variation in use by race or ethnicity because white women may have better access to new information (11). Finally, because the WHI study results were specific to women taking continuous estrogen plus progestin, we hypothesized that hormone use would be more stable among women who had had hysterectomies because such women typically take only estrogen and may believe that the findings do not apply to them. Methods Sample The San Francisco Mammography Registry is a population-based registry of women undergoing mammography in San Francisco, California. It is 1 of 7 registries participating in the National Cancer Institute Breast Cancer Surveillance Consortium (12). This registry began to prospectively collect patient data and mammography results in 1995 and currently captures about 90% of mammography examinations performed in San Francisco. Data from 11 mammography facilities are included in this analysis. Women were eligible for this analysis if they were between the ages of 50 to 74 years, were postmenopausal, did not report a personal history of breast cancer, and underwent screening or diagnostic mammography between January 1997 and 19 May 2003. Women 55 years of age and older were assumed to be menopausal. Women 50 to 54 years of age were considered to be menopausal if both ovaries had been removed or if they reported that their periods had stopped permanently. For women who had mammography more than once in any calendar year, we included only the first instance of mammography in that year to prevent overrepresentation of women undergoing an evaluation of an abnormal mammogram because this experience may influence use of hormone therapy. Our final sample included 15 1862 mammograms received by 71 219 women. Data At the facilities that participate in the San Francisco Mammography Registry, each woman completes a brief, scannable questionnaire at the time of mammography. This questionnaire collects information about current use of hormone therapy and several personal characteristics, including race or ethnicity (categorized as white, African American, Latina, Chinese, Filipina, other Asian, other), family history of breast cancer (including mother, sisters, and daughters), history of childbirth, whether the woman had undergone a hysterectomy, menopausal status, history of breast biopsy (including fine-needle aspiration, core biopsy, and surgical biopsy). Information about age at the time of mammography, date of mammography, and ZIP code of residence is reported by the facility. Data from the year 2000 U.S. Census was used to assign median income for each womans ZIP code of residence as a proxy for socioeconomic status. Variables Our outcome variable for this analysis was the current use of hormone therapy. Date of mammography was represented as a linear term. Binary variables were created for the publication dates of HERS (before 19 August 1998 vs. that date or later) and the publication of the principal findings from the WHI (before 17 July 2002 vs. that date or later). Other independent variables examined were age at the time of mammography, race or ethnicity, median income of the ZIP code of residence, history of childbirth, family history of breast cancer, history of breast biopsy, and previous hysterectomy. Statistical Analysis Because some of the women in this sample had more than 1 mammogram represented in this data set, which spanned a 7-year period, we conducted a repeated-measures logistic regression to adjust the variance estimates for clustering of hormone therapy use over time for individual women and for the clustering of women within mammography facilities (13). Generalized estimating equations were implemented by using the SUDAAN statistical package, version 8.0.0 (Research Triangle Institute, Research Triangle Park, North Carolina) assuming an exchangeable correlation matrix. These models included a linear term indicating quarter from January 1997 to the first quarter of 2003 to control for temporal trends (the last quarter included mammograms through 19 May 2003), the variables specified above to indicate the dates of publication of HERS and the WHI, and an interaction term between each of these publication indicators and the time (in quarters) following each of these publications to measure changes in use after the publication of these clinical trials. These models also controlled for the individual characteristics described earlier (that is, age, race or ethnicity, history of childbirth, family history of breast cancer, history of breast biopsy, previous hysterectomy, median income for the ZIP code of residence). To specifically test our hypotheses about differential changes in the use of hormone therapy for subgroups of women on the basis of age, hysterectomy status, and race or ethnicity, we examined interaction terms to test for effect modification. For the main effects, a P value less than 0.05 was considered statistically significant, and for the interaction terms, a P value less than 0.01 was considered to be statistically significant. The likelihood ratio test compared the null model with the fitted model. Role of the Funding Source This work was supported by a National Cancer Institutefunded Breast Cancer Surveillance Consortium agreement. The funding source did not participate in the design, conduct, or reporting of this analysis or in the decision to submit the manuscript for publication. Results The Table shows the characteristics of the sample for each of the study years. Over the time period of the study, the median age decreased from 61 years to 59 years. The racial and ethnic composition of the sample also changed somewhat across the study years. Fewer women undergoing mammography in 2003 reported a history of childbirth (71.7% vs. 75.2%) or hysterectomy than did women undergoing mammography in 1997. Conversely, more women reported a family history of breast cancer (17.1% vs. 11.8%) or a personal history of a previous breast biopsy or aspiration. The average number of mammograms obtained for each woman in our sample across the 7-year study period was 2.1 (range, 1 to 7). Table. Description of the Sample (151 862 Mammograms) The Figure shows the unadjusted rates of current hormone therapy use by month for all of the women in the sample. Among menopausal women who had received mammography, we estimated that the average proportion reporting the current use of hormone therapy was 41% in 1997. In 1997, hormone use was highest among white women (52.6%) and lowest among African-American women (34.1%), Latina women (33.9%), Chinese women (32.2%), and Filipina women (29.6%). In 1997, hormone use was higher among younger women than older women (48.7% vs. 28.7%; P < 0.001) and among women who had had a hysterectomy compared with women who had not had a hysterectomy (60.0% vs. 36.4%; P < 0.001). Figure. Rates of hormone therapy use among postmenopausal women, 1997 to 2003 HERS WHI The adjusted multivariate model estimates that before the publication of HERS, the use of hormone thera

Does Utilization of Screening Mammography Explain Racial and Ethnic Differences in Breast Cancer

Context Breast cancer mortality rates have fallen but still differ by race and ethnicity. One explanation might be differences in mammography use. Content These investigators linked data from mammography registries to tumor registries and showed that African-American and Hispanic women have longer intervals between mammography and are more likely to have advanced-stage tumors at diagnosis and to die of breast cancer than white women. However, in women with similar screening histories, these rates were similar regardless of race or ethnicity. Implications Differences in mammography use may explain ethnic disparities in the incidence of advanced-stage breast cancer and in mortality rates. The Editors Breast cancer mortality rates in the United States began to decrease in the 1990s (1) because of increased use of screening mammography and improved breast cancer treatment (2, 3). However, these decreases have primarily benefited non-Hispanic white women, whereas the mortality rate for breast cancer in African-American women changed little (1). Although racial and ethnic differences in breast cancer mortality rates have been consistently documented (1, 4-9), reasons for the persistence of these differences have been difficult to ascertain (10). Possible explanations include differences in biological characteristics of tumors (11-13); patient characteristics, such as obesity, that may affect prognosis; mammography use (14, 15); timeliness and completeness of breast cancer diagnosis and treatment (16, 17); social factors, such as education, literacy, and cultural beliefs; and economic factors, such as income level and health insurance coverage, that might affect a patients access to and choices for breast cancer screening and treatment (18-22). Stage at diagnosis, the strongest predictor of breast cancer survival (23), is proportionally higher in all non-Asian minority groups than in white women (8). Although minority women have historically undergone less mammography than white women (14), several recent surveys have found only small differences in mammography use between white and nonwhite women (24, 25). These observations raised doubt that tumors go undiagnosed until later stages in minority women because of infrequent breast cancer screening (26). However, the 2 most widely cited surveys of mammography use are based on self-report and only inquire about recent use, not adherence over time (24, 25). We explored stage of disease at diagnosis, tumor characteristics (including size and grade), and lymph node involvement among women of different races and ethnicities whose patterns of mammography use were similar. We hypothesized that differences in tumor characteristics may result primarily from differences in mammography use and that women with similar patterns of mammography use may have similar tumor characteristics. We had sufficient sample sizes within each racial and ethnic group and obtained sufficiently detailed data regarding mammography use to permit stratification of the cohort by pattern of mammography use; this technique enabled us to compare tumor characteristics among women with similar screening histories. Methods Data Source We pooled data from facilities that participate in 7 mammography registries that form the National Cancer Institutefunded Breast Cancer Surveillance Consortium: San Francisco Mammography Registry, San Francisco, California; Group Health Cooperative, Seattle, Washington; Colorado Mammography Project, Denver, Colorado; Vermont Breast Cancer Surveillance System, Burlington, Vermont; New Hampshire Mammography Network, Lebanon, New Hampshire; Carolina Mammography Registry, Chapel Hill, North Carolina; and New Mexico Mammography Project, Albuquerque, New Mexico. The data consisted of information sent to the registries regarding all mammographic evaluations performed at these facilities, including radiology reports and breast health surveys. The surveys, which were completed by patients at each mammography examination, included questions regarding race, ethnicity, presence of breast symptoms, and previous mammography use. Breast cancer diagnoses and tumor characteristics were obtained through linkage with state tumor registries; regional Surveillance, Epidemiology, and End Results programs; and hospital-based pathology services. Previous research has shown that at least 94% of cancer cases are identified through these linkages (27). Each surveillance registry captures most mammography case reports within its respective geographic area, and mammograms in these registries include approximately 2% of mammographic examinations performed in the United States. Each registry obtains annual approval from its institutional review board to collect mammography-related information and to link with tumor registries. Participants This study included women without a history of breast cancer who were 40 years of age and older who had undergone mammography at least once for screening or diagnostic purposes between 1996 and 2002 (n= 1010515). We categorized the race and ethnicity of the participating women (the mammography registry cohort) as non-Hispanic white (n= 789997), non-Hispanic African American/black (n= 62408), Hispanic (n= 90642), Asian/Pacific Islander (n= 49867), or Native American/Native Alaskan (n= 17601). We excluded women who did not report their race or ethnicity (n= 133235 [12%]) or reported mixed or other race (n= 6003 [<1%]). Breast cancer was diagnosed in a subset of the women in the mammography registry cohort (Table 1). Table 1. General Categorization of Study Participants Characterization of Mammography Use We included all mammographic evaluations in eligible women that were performed during the study period. We characterized each mammogram that was included in the study by the time interval between that mammogram and the one most recently preceding it. We determined these intervals by using examination dates that were recorded in the database (data were available for 85% of patients) and self-reported dates that the remaining women provided at the time of their examination. The mammography screening intervals were categorized into the following groups: within 1 year (4 to 17 months); 2 years (18 to 29 months); 3 years (30 to 41 months); and 4 years or longer (>41 months). At the time of each mammogram, women completed a breast health survey and provided the date of their last mammogram. We created 2 classifications for first mammograms. Mammography was classified as a first screening if the radiologist coded the examination as screening and the woman reported no breast symptoms. The mammogram was classified as diagnostic if the radiologist coded the examination as diagnostic or if the woman reported a breast mass or nipple discharge. Women whose first mammogram was diagnostic were assigned to the never screened group. Of note, a woman could have had mammography more than once during the study period and therefore could contribute more than 1 observation to the analyses. A woman could have observations that were categorized into different mammography screening intervals. For example, a woman could have had her first mammographic evaluation in 1998 and had subsequent mammography in 1999 and 2001. Her first mammogram would have been categorized as a first screening or as diagnostic, depending on the radiologists indication for that examination and whether the patient reported symptoms. Her second mammogram would have been categorized in the 1 year group, and her third mammography would have been categorized in the 2 year group. Breast Cancer To determine breast cancer status, we tracked each participants mammogram for 365 days following the date it had been obtained or until the patient underwent her next mammographic examination (whichever came first). Consequently, each tumor was associated with a single mammogramthat obtained closest to the date of diagnosis. We characterized breast cancer as either invasive or ductal carcinoma in situ. Large tumors were defined as invasive tumors that were 15 mm or larger in diameter. We used the TNM (tumor, node, metastasis) system (which is based on the criteria of the American Joint Committee on Cancer) to classify stage at diagnosis as 0 (ductal carcinoma in situ), 1, 2, 3, or 4 (28); advanced-stage tumors were defined as invasive lesions of stage 2 or higher. High-grade tumors were defined as invasive lesions of grades 3 and 4. Lymph node status was defined as positive, negative, or unknown. Advanced disease was defined as the presence of a large, advanced-stage, high-grade tumor or lymph nodepositive tumor at the time of diagnosis. Statistical Analysis We calculated the frequency distributions of various risk factors for all women in the mammography registry cohort. Among the subset of women with breast cancer (n= 17558), we calculated the proportion of tumors that were invasive and, among invasive tumors, the proportion that were advanced-stage or high-grade tumors; we then calculated the distribution by race and ethnicity. For all women in the cohort, we evaluated whether overall and advanced cancer rates per 1000 mammograms were similar across racial and ethnic groups after we adjusted for age and registry by using Poisson regression. We then calculated whether adjusted overall and advanced cancer rates per 1000 mammograms were similar across mammography screening interval groups. Because overall and advanced cancer rates varied across racial and ethnic groups (P< 0.001) and by previous mammography use (P< 0.001), and because mammography use potentially varied by race and ethnicity, we modeled cancer rates among similarly screened women in each ethnic group. We used Poisson regression to adjust for age and registry; an interaction term between race and ethnicity and previous mammography use was included in the Poisson model to allow for possible differences in the association between ethnicity and cancer rates by mammography group

Biomarker Expression and Risk of Subsequent Tumors After Initial Ductal Carcinoma In Situ Diagnosis

BACKGROUND Studies have failed to identify characteristics of women who have been diagnosed with ductal carcinoma in situ (DCIS) and have a high or low risk of subsequent invasive cancer. METHODS We conducted a nested case-control study in a population-based cohort of 1162 women who were diagnosed with DCIS and treated by lumpectomy alone from 1983 to 1994. We collected clinical characteristics and information on subsequent tumors, defined as invasive breast cancer or DCIS diagnosed in the ipsilateral breast containing the initial DCIS lesion or at a regional or distant site greater than 6 months after initial treatment of DCIS (N = 324). We also conducted standardized pathology reviews and immunohistochemical staining for the estrogen receptor (ER), progesterone receptor, Ki67 antigen, p53, p16, epidermal growth factor receptor-2 (ERBB2, HER2/neu oncoprotein), and cyclooxygenase-2 (COX-2) on the initial paraffin-embedded DCIS tissue. Competing risk models were used to determine factors associated with risk of subsequent invasive cancer vs DCIS, and cumulative incidence survival functions were used to estimate 8-year risk. RESULTS Factors associated with subsequent invasive cancer differed from those associated with subsequent DCIS. Eight-year risk of subsequent invasive cancer was statistically significantly (P = .018) higher for women with initial DCIS lesions that were detected by palpation or that were p16, COX-2, and Ki67 triple positive (p16(+)COX-2(+)Ki67(+)) (19.6%, 95% confidence interval [CI] = 18.0% to 21.3%) than for women with initial lesions that were detected by mammography and were p16, COX-2, and Ki67 triple negative (p16(-)COX-2(-)Ki67(-)) (4.1%, 95% CI = 3.4% to 5.0%). In a multivariable model, DCIS lesions that were p16(+)COX-2(+)Ki67(+) or those detected by palpation were statistically significantly associated with subsequent invasive cancer, but nuclear grade was not. Eight-year risk of subsequent DCIS was highest for women with DCIS lesions that had disease-free margins of 1 mm or greater combined with either ER(-)ERBB2(+)Ki67(+) or p16(+)COX-2(-)Ki67(+) status (23.6%, 95% CI = 18.1% to 34.0%). CONCLUSION Biomarkers can identify which women who were initially diagnosed with DCIS are at high or low risk of subsequent invasive cancer, whereas histopathology information cannot.

Effect of Antihypertensive Treatment in Patients Having Already Suffered From Stroke: Gathering the Evidence

BACKGROUND AND PURPOSE Drug treatment of high blood pressure has been shown to reduce the associated cardiovascular risk. Stroke represents the type of event more strongly linked with high blood pressure, responsible for a high rate of death or invalidity, and with the highest proportion of events that can be avoided by treatment. Hypertensive patients with a history of cerebrovascular accident are at particularly high risk of recurrence. Specific trials of blood pressure lowering drugs in stroke survivors showed inconclusive results in the past. METHODS We performed a meta-analysis using all available randomized controlled clinical trials assessing the effect of blood pressure lowering drugs on clinical outcomes (recurrence of stroke, coronary events, cause-specific, and overall mortality) in patients with prior stroke or transient ischemic attack. RESULTS We identified 9 trials, including a total of 6752 patients: 2 trials included 551 hypertensive stroke survivors; 6 trials of hypertensive patients included a small proportion of stroke survivors (536 patients); 1 trial included stroke survivors, whether hypertensive or not (5665 patients). The recurrence of stroke, fatal and nonfatal, was significantly reduced in active groups compared with control groups consistently across the different sources of data (relative risk of 0.72, 95% confidence interval: 0.61 to 0.85). There was no evidence that this intervention induced serious adverse effect. CONCLUSIONS Blood pressure lowering drug interventions reduced the risk of stroke recurrence in stroke survivors. Available data did not allow to verify whether such benefit depends on initial blood pressure level. More data are needed before considering antihypertensive therapy in normotensive patients at high cerebrovascular risk.

Cost-Effectiveness of Extending Screening Mammography Guidelines To Include Women 40 to 49 Years of Age

There is universal agreement [1-4] that women 50 to 69 years of age should undergo screening mammography because randomized, controlled trials have shown that such screening reduces breast cancer mortality in this age group [5, 6]. This consensus is bolstered by the results of cost-effectiveness analyses that consistently show that this benefit can be achieved at a reasonable cost [7-9]. In contrast, whether women 40 to 49 years of age should undergo screening mammography is controversial [10-15]. Pooled results of large randomized, controlled trials have shown no mortality reduction in 40- to 49-year-old women after 7 to 9 years of screening [5, 16-18]. However, a statistically significant reduction in breast cancer mortality becomes apparent 10 to 14 years after the initiation of screening [19]. Some authors have argued that this delayed benefit should not be ignored [11]. However, the reality of constrained health care resources requires that any benefit from preventive services be achieved at a reasonable cost. Two recently published analyses [20, 21] suggest that mammographic screening in younger women may be as cost-effective as screening in older women. The first analysis [20] calculated average cost-effectiveness by comparing a strategy of screening 40- to 69-year-old women with no screening. Most of the benefit achieved by using this strategy occurs when women are 50 to 69 years of age. Therefore, this analysis did not address whether it is cost-effective to screen women from 40 to 49 years of age in addition to screening them from 50 to 69 years of age. To determine whether the additional benefit obtained by extending screening mammography to women 40 to 49 years of age comes at a reasonable cost would require an incremental cost-effectiveness analysis [22-26]. The second analysis [21] used a simplified life-expectancy accounting method, did not discount costs or benefits, and associated screening mammography with unsubstantiated mortality reductions (30% for the base case). Neither analysis [20, 21] included an important aspect of the results of screening mammography trials in 40- to 49-year-old women: that is, no benefit occurs until 10 years after the initiation of screening. An earlier analysis [10] found screening mammography to be more expensive in women 40 to 49 years of age than in women 50 years of age and older. This previous analysis calculated incremental cost-effectiveness, discounted costs and benefits, and included an estimated delay between the onset of screening and the onset of a mortality benefit. Our analysis extends this work by including updated pooled results of the randomized, controlled trials [19]; actual delay times before the onset of benefits; and updated costs of mammography and treatment of breast cancer. Methods Model We developed a Markov model [27, 28] that compared the life expectancy of women undergoing different breast cancer screening strategies. Except for women in whom breast cancer was diagnosed at the initiation of screening, women were healthy at entry into the model. At the end of each 1-year cycle, women were in one of four health states: They remained healthy, developed breast cancer and remained alive, died of breast cancer, or died of another cause. The transition probabilities [that is, the probabilities of developing breast cancer, dying of breast cancer, and dying of another cause] were both age- and strategy-dependent. The base-case analysis compared three strategies: 1) no screening; 2) screening biennially from 50 to 69 years of age; and 3) screening every 18 months from 40 to 49 years of age, followed by screening biennially from 50 to 69 years of age. The rationale for these screening intervals is discussed below. We calculated the cost-effectiveness of screening in women 50 years of age and older by comparing the first strategy with the second strategy. To determine the incremental cost-effectiveness of screening in 40- to 49-year-old women, we compared the second and third strategies. Costs and benefits were discounted at a rate of 3% per year for the base-case analysis [23]. Benefits Trials of screening mammography have shown no reduction in breast cancer mortality among screened women until several years after the initiation of screening [5, 18, 29, 30]. Meta-analyses and one pooled analysis have shown that among 40- to 49-year-old women, the summary relative risk reduction in breast cancer mortality 7 to 9 years after the initiation of screening is about 1, indicating no reduction in mortality [5, 16-18]. Ten to 12 years after the initiation of screening, a nonsignificant trend toward reduced mortality is evident in the screened group (Figure 1, left) [5, 18, 29, 30]. Recently updated results show a statistically significant 16% reduction that occurs 10 to 14 years after the initiation of screening [19]. For women 50 to 69 years of age, there is an initial period of about 5 years that shows no benefit from screening (Figure 1, right) [18, 29, 30]. Figure 1. Cumulative breast cancer mortality in screened (black circles) compared with nonscreened (white circles) women. Left. Right. In our model, for women who start screening at 50 years of age, a 27% reduction in breast cancer mortality (Table 1) [5] begins 5 years after the initiation of screening and continues until age 74 years. Although screening ends at 69 years of age, we assumed that women would continue to benefit for another 5 years because of early detection of breast cancer in the last years of screening. For women who begin screening at 40 years of age, a 16% reduction in breast cancer mortality starts at age 50 years; this reduction increases to 27% at age 55 years. Table 1. Information Used To Calculate Life Expectancy Screening Interval The screening interval in randomized, controlled trials of screening mammography has varied from 12 to 33 months for women 50 years of age and older. Pooled results of the efficacy of mammography stratified by length of screening interval do not differ for women in this age group [5]. From published results [5], we determined a 28% (95% CI, 15% to 31%) reduction in breast cancer mortality in women 50 years of age and older who were screened every 18 to 33 months and a 25% (CI, 1% to 43%) reduction in those screened every 12 months. For the base-case analysis, we therefore chose to perform biennial screening in women 50 years of age and older because screening more often only increases cost without increasing the benefits of screening. For the base-case analysis, we used the pooled reduction in breast cancer mortality (27%) [5] from all randomized, controlled trials to determine the cost-effectiveness of biennial screening in 50- to 69-year-old women; in a sensitivity analysis, we determined the cost-effectiveness of annual screening. The screening interval in randomized, controlled trials has varied from 12 to 24 months for women 40 to 49 years of age. Pooled results of the efficacy of screening mammography stratified by length of screening interval did not show a statistically significant reduction in breast cancer mortality for 12-month or 18- to 24-month screening intervals [5]. As noted above, recently reported pooled results of all randomized, controlled trials, which on average used a screening interval of 18 months, showed a statistically significant 16% reduction in breast cancer mortality 10 to 14 years after the initiation of screening [19]. We therefore assumed that a 16% reduction in breast cancer mortality would be achieved with screening done every 18 months. This screening interval is consistent with the guidelines of organizations [2, 3] that recommend screening every 1 to 2 years for 40- to 49-year-old women. In sensitivity analyses, we calculated the cost-effectiveness of annual and biennial screening, assuming the same 16% reduction in breast cancer mortality among screened women. Utilities Because there are few data on the utility that women place on life after treatment of breast cancer or the utility placed on living with metastatic breast cancer, we did not include utilities in the base-case analysis. Data from a small Australian study [33] (which observed a utility of about 0.8 for life after treatment of breast cancer and a utility of about 0.3 for life with metastatic cancer) are included in a sensitivity analysis to determine the extent to which cost per year of life saved might differ from cost per quality-adjusted life-year saved. Costs We included three costs: the cost of screening mammographic examinations, the cost of evaluating abnormal mammograms, and the cost of treating breast cancer (Table 2). Additional details on derivations of costs are given in the Appendix. The cost of screening mammography was based on the average cost (

Cumulative Probability of False-Positive Recall or Biopsy Recommendation After 10 Years of Screening Mammography: A Cohort Study

91) reported by the National Cancer Institutes National Survey of Mammography Facilities [34]. This cost was inflated to 1995 dollars (

Personalizing Mammography by Breast Density and Other Risk Factors for Breast Cancer: Analysis of Health Benefits and Cost-Effectiveness

106) by using the consumer price index for medical services. We assumed that women in whom breast cancer was diagnosed continued to undergo screening mammography of the opposite breast, at the same cost, after the initial diagnosis of breast cancer. Table 2. Information Used to Calculate Costs The cost of evaluating abnormal mammographic results was calculated as a weighted average of procedures that may follow abnormal mammograms. This cost was also inflated to 1995 dollars. The distribution and types of follow-up procedures were based on those reported by the National Cancer Institutes National Survey of Mammography Facilities [35]. A range of costs for each procedure was based on data from Medicare, Pennsylvania Blue Cross, Group Health Cooperative, and Kaiser Permanente (Brown M. Personal communication). The percentage of abnormal mammograms was based on the percentage seen with high-quality modern screening mammography (Table 2) [36]. Population-based data on the cancer stage at diagnosis in screened compared with nonscreened women are sparse. O