Rachel Ballard-Barbash

Overweight, Obesity, and Mortality in a Large Prospective Cohort of Persons 50 to 71 Years Old

BACKGROUND Obesity, defined by a body-mass index (BMI) (the weight in kilograms divided by the square of the height in meters) of 30.0 or more, is associated with an increased risk of death, but the relation between overweight (a BMI of 25.0 to 29.9) and the risk of death has been questioned. METHODS We prospectively examined BMI in relation to the risk of death from any cause in 527,265 U.S. men and women in the National Institutes of Health-AARP cohort who were 50 to 71 years old at enrollment in 1995-1996. BMI was calculated from self-reported weight and height. Relative risks and 95 percent confidence intervals were adjusted for age, race or ethnic group, level of education, smoking status, physical activity, and alcohol intake. We also conducted alternative analyses to address potential biases related to preexisting chronic disease and smoking status. RESULTS During a maximum follow-up of 10 years through 2005, 61,317 participants (42,173 men and 19,144 women) died. Initial analyses showed an increased risk of death for the highest and lowest categories of BMI among both men and women, in all racial or ethnic groups, and at all ages. When the analysis was restricted to healthy people who had never smoked, the risk of death was associated with both overweight and obesity among men and women. In analyses of BMI during midlife (age of 50 years) among those who had never smoked, the associations became stronger, with the risk of death increasing by 20 to 40 percent among overweight persons and by two to at least three times among obese persons; the risk of death among underweight persons was attenuated. CONCLUSIONS Excess body weight during midlife, including overweight, is associated with an increased risk of death.

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

Physical Activity, Biomarkers, and Disease Outcomes in Cancer Survivors: A Systematic Review

BACKGROUND Cancer survivors often seek information about how lifestyle factors, such as physical activity, may influence their prognosis. We systematically reviewed studies that examined relationships between physical activity and mortality (cancer-specific and all-cause) and/or cancer biomarkers. METHODS We identified 45 articles published from January 1950 to August 2011 through MEDLINE database searches that were related to physical activity, cancer survival, and biomarkers potentially relevant to cancer survival. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement to guide this review. Study characteristics, mortality outcomes, and biomarker-relevant and subgroup results were abstracted for each article that met the inclusion criteria (ie, research articles that included participants with a cancer diagnosis, mortality outcomes, and an assessment of physical activity). RESULTS There was consistent evidence from 27 observational studies that physical activity is associated with reduced all-cause, breast cancer-specific, and colon cancer-specific mortality. There is currently insufficient evidence regarding the association between physical activity and mortality for survivors of other cancers. Randomized controlled trials of exercise that included biomarker endpoints suggest that exercise may result in beneficial changes in the circulating level of insulin, insulin-related pathways, inflammation, and, possibly, immunity; however, the evidence is still preliminary. CONCLUSIONS Future research directions identified include the need for more observational studies on additional types of cancer with larger sample sizes; the need to examine whether the association between physical activity and mortality varies by tumor, clinical, or risk factor characteristics; and the need for research on the biological mechanisms involved in the association between physical activity and survival after a cancer diagnosis. Future randomized controlled trials of exercise with biomarker and cancer-specific disease endpoints, such as recurrence, new primary cancers, and cancer-specific mortality in cancer survivors, are warranted.

Elevated Biomarkers of Inflammation Are Associated With Reduced Survival Among Breast Cancer Patients

PURPOSE Chronic inflammation is believed to contribute to the development and progression of breast cancer. Systemic C-reactive protein (CRP) and serum amyloid A (SAA) are measures of low-grade chronic inflammation and potential predictors of cancer survival. PATIENTS AND METHODS We evaluated the relationship between circulating markers of inflammation and breast cancer survival using data from the Health, Eating, Activity, and Lifestyle (HEAL) Study (a multiethnic prospective cohort study of women diagnosed with stage 0 to IIIA breast cancer). Circulating concentrations of CRP and SAA were measured approximately 31 months after diagnosis and tested for associations with disease-free survival (approximately 4.1 years of follow-up) and overall survival (approximately 6.9 years of follow-up) in 734 disease-free breast cancer survivors. Cox proportional hazards models were used with adjustment for potential confounding factors to generate hazard ratios (HRs) and 95% CIs. Results Elevated SAA and CRP were associated with reduced overall survival, regardless of adjustment for age, tumor stage, race, and body mass index (SAA P trend < .0001; CRP P trend = .002). The HRs for SAA and CRP tertiles suggested a threshold effect on survival, rather than a dose-response relationship (highest v lowest tertile: SAA HR = 3.15; 95% CI, 1.73 to 5.65; CRP HR = 2.27; 95% CI, 1.27 to 4.08). Associations were similar and still significant after adjusting for self-reported history of cardiovascular events and censoring cardiovascular disease deaths. Elevated CRP and SAA were also associated with reduced disease-free survival, although these associations were of borderline significance (SAA P trend = .04; CRP P trend = .07). CONCLUSION Circulating SAA and CRP may be important prognostic markers for long-term survival in breast cancer patients, independent of race, tumor stage, and body mass index.

Annual Report to the Nation on the status of cancer, 1975-2008, featuring cancers associated with excess weight and lack of sufficient physical activity†‡

Annual updates on cancer occurrence and trends in the United States are provided through collaboration between the American Cancer Society (ACS), the Centers for Disease Control and Prevention (CDC), the National Cancer Institute (NCI), and the North American Association of Central Cancer Registries (NAACCR). This years report highlights the increased cancer risk associated with excess weight (overweight or obesity) and lack of sufficient physical activity (<150 minutes of physical activity per week).

Influence of Pre- and Postdiagnosis Physical Activity on Mortality in Breast Cancer Survivors: The Health, Eating, Activity, and Lifestyle Study

PURPOSE To investigate the association between pre- and postdiagnosis physical activity (as well as change in prediagnosis to postdiagnosis physical activity) and mortality among women with breast cancer. PATIENTS AND METHODS This was a prospective observational study of 933 women enrolled onto the Health, Eating, Activity, and Lifestyle Study who were diagnosed with local or regional breast cancer between 1995 and 1998 and observed until death or September 2004, whichever came first. The primary outcomes measured were total deaths and breast cancer deaths. The primary exposures were physical activity in the year before and 2 years after diagnosis and the pre- to postdiagnosis change in physical activity. RESULTS Compared with inactive women, the multivariable hazard ratios (HRs) for total deaths for women expending at least 9 metabolic equivalent hours per week (approximately 2 to 3 h/wk of brisk walking) were 0.69 (95% CI, 0.45 to 1.06; P = .045) for those active in the year before diagnosis and 0.33 (95% CI, 0.15 to 0.73; P = .046) for those active 2 years after diagnosis. Compared with women who were inactive both before and after diagnosis, women who increased physical activity after diagnosis had a 45% lower risk of death (HR = 0.55; 95% CI, 0.22 to 1.38), and women who decreased physical activity after diagnosis had a four-fold greater risk of death (HR = 3.95; 95% CI, 1.45 to 10.50). CONCLUSION Moderate-intensity physical activity after a diagnosis of breast cancer may improve prognosis.

Adiposity and Sex Hormones in Postmenopausal Breast Cancer Survivors

PURPOSE Overweight and obese women with breast cancer have poorer survival compared with thinner women. One possible reason is that breast cancer survivors with higher degrees of adiposity have higher concentrations of tumor-promoting hormones. This study examined the association between adiposity and concentrations of estrogens, androgens, and sex hormone-binding globulin (SHBG) in a population-based sample of postmenopausal women with breast cancer. METHODS We studied the associations between body mass index (BMI), body fat mass, and percent body fat, measured by dual-energy x-ray absorptiometry scan, waist circumference, and waist-to-hip circumference ratio, with concentrations of estrone, estradiol, testosterone, SHBG, dehydroepiandrosterone sulfate, free estradiol, and free testosterone in 505 postmenopausal women in western Washington and New Mexico with incident stage 0 to IIIA breast cancer. Blood and adiposity measurements were performed between 4 and 12 months after diagnosis. RESULTS Obese women (BMI > or = 30) had 35% higher concentrations of estrone and 130% higher concentrations of estradiol compared with lighter-weight women (BMI < 22.0; P =.005 and.002, respectively). Similar associations were observed for body fat mass, percent body fat, and waist circumference. Testosterone concentrations also increased with increasing levels of adiposity (P =.0001). Concentrations of free estradiol and free testosterone were two to three times greater in overweight and obese women compared with lighter-weight women (P =.0001). CONCLUSION These data provide information about potential hormonal explanations for the association between adiposity and breast cancer prognosis. These sex hormones may be useful biomarkers for weight loss intervention studies in women with breast cancer.

Changes in Body Fat and Weight After a Breast Cancer Diagnosis: Influence of Demographic, Prognostic, and Lifestyle Factors

PURPOSE Obese women and women who gain weight after a breast cancer diagnosis are at a greater risk for breast cancer recurrence and death compared with lean women and women who do not gain weight after diagnosis. In this population-based study, we assessed weight and body fat changes from during the first year of diagnosis to during the third year after diagnosis, and whether any changes in weight and body fat varied by demographic, prognostic, and lifestyle factors in 514 women with incident Stage 0-IIIA breast cancer. METHODS Patients were participants in the Health, Eating, Activity, and Lifestyle (HEAL) study. Weight and body fat (via dual-energy x-ray absorptiometry scans) were measured during the baseline visit and 2 years later at a follow-up visit. Analysis of covariance methods were used to obtain mean weight and body fat changes adjusted for potential cofounders. RESULTS Women increased their weight and percent body fat by 1.7 +/- 4.7 kg and 2.1% +/- 3.9%, respectively, from during their first year of diagnosis to during their third year of diagnosis. A total of 68% and 74% of patients gained weight and body fat, respectively. Greater increases in weight were observed among women diagnosed with a higher disease stage, younger age, being postmenopausal, and women who decreased their physical activity from diagnosis to up to 3 years after diagnosis (P for trend < .05). CONCLUSION Weight and body fat increased in the postdiagnosis period. Future research should focus on the effect of physical activity on weight and fat loss and breast cancer prognosis.

Physical activity levels among breast cancer survivors

Implications for Muscle Lipid Metabolism and An accumulation of intramuscular lipid has been reported with obesity and linked with insulin resistance. The purpose of this paper is to discuss: 1) mechanisms that may be responsible for intramuscular lipid accumulation with obesity, and 2) the effects of common interventions (weight loss or exercise) for obesity on skeletal muscle lipid metabolism and intramuscular lipid content. Data suggest that the skeletal muscle of morbidly obese humans is characterized by the preferential partitioning of lipid toward storage rather than oxidation. This phenotype may, in part, contribute to increased lipid deposition in both muscle and adipose tissue, and promote the development of morbid obesity and insulin resistance. Weight loss intervention decreases intramuscular lipid content, which may contribute to improved insulin action. On the other hand, exercise training improves insulin action and increases fatty acid oxidation in the skeletal muscle of obese/morbidly obese individuals. In summary, the accumulation of intramuscular lipid appears to be detrimental in terms of inducing insulin resistance; however, the accumulation of lipid can be reversed with weight loss. The mechanism(s) by which exercise enhances insulin action remains to be determined.INTRODUCTION/PURPOSE The Talk Test has been shown to be well correlated with the ventilatory threshold, with accepted guidelines for exercise prescription, and with the ischemic threshold. As such, it appears to be a valuable although quite simple method of exercise prescription. In this study, we evaluate the consistency of the Talk Test by comparing responses during different modes of exercise. METHODS Healthy volunteers (N = 16) performed incremental exercise, on both treadmill and cycle ergometer. Trials were performed with respiratory gas exchange and while performing the Talk Test. Comparisons were made regarding the correspondence of the last positive, equivocal, and first negative stages of the Talk Test with ventilatory threshold. RESULTS The %VO2peak, %VO2 reserve, %HRpeak, and %HR reserve at ventilatory threshold on treadmill versus cycle ergometer (77%, 75%. 89%, and 84% vs 67%, 64%, 82%, and 74%) were not significantly different than the equivocal stage of the Talk Test (83%, 82%, 86%, and 80% vs 73%, 70%, 87%, and 81%). The VO2 at ventilatory threshold and the last positive, equivocal and negative stages of the Talk Test were well correlated during treadmill and cycle ergometer exercise. CONCLUSIONS The results support the hypothesis that the Talk Test approximates ventilatory threshold on both treadmill and cycle. At the point where speech first became difficult, exercise intensity was almost exactly equivalent to ventilatory threshold. When speech was not comfortable, exercise intensity was consistently above ventilatory threshold. These results suggest that the Talk Test may be a highly consistent method of exercise prescription.INTRODUCTION Obesity and weight gain are negative prognostic factors for breast cancer survival. Physical activity (PA) prevents weight gain and may decrease obesity. Little information exists on PA levels among cancer survivors. We assessed PA, including the proportion of breast cancer survivors engaging in recommended levels, by categories of adiposity, age, disease stage, and ethnicity in 806 women with stage 0-IIIA breast cancer participating in the Health, Eating, Activity, and Lifestyle Study. METHODS Black, non-Hispanic white, and Hispanic breast cancer survivors were recruited into the study through Surveillance Epidemiology End Results registries in New Mexico, Western Washington, and Los Angeles County, CA. Types of sports and household activities and their frequency and duration within the third yr after diagnosis were assessed during an in-person interview. RESULTS Thirty-two percent of breast cancer survivors participated in recommended levels of PA defined as 150 min x wk(-1) of moderate- to vigorous-intensity sports/recreational PA. When moderate-intensity household and gardening activities were included in the definition, 73% met the recommended level of PA. Fewer obese breast cancer survivors met the recommendation than overweight and lean breast cancer survivors (P < 0.05). Fewer black breast cancer survivors met the recommendation compared with non-Hispanic white and Hispanic breast cancer survivors (P < 0.05). CONCLUSIONS Most of the breast cancer survivors were not meeting the PA recommendations proposed for the general adult population. Efforts to encourage and facilitate PA among these women would be an important tool to decrease obesity, prevent postdiagnosis weight gain, and improve breast cancer prognosis.PURPOSE To derive a regression equation that estimates metabolic equivalent (MET) from accelerometer counts, and to define thresholds of accelerometer counts that can be used to delineate sedentary, light, moderate, and vigorous activity in adolescent girls. METHODS Seventy-four healthy 8th grade girls, age 13 - 14 yr, were recruited from urban areas of Baltimore, MD, Minneapolis/St. Paul, MN, and Columbia, SC, to participate in the study. Accelerometer and oxygen consumption (.-)VO(2)) data for 10 activities that varied in intensity from sedentary (e.g., TV watching) to vigorous (e.g., running) were collected. While performing these activities, the girls wore two accelerometers, a heart rate monitor and a Cosmed K4b2 portable metabolic unit for measurement of (.-)VO(2). A random-coefficients model was used to estimate the relationship between accelerometer counts and (.-)VO(2). Activity thresholds were defined by minimizing the false positive and false negative classifications. RESULTS The activities provided a wide range in (.-)VO(2) (3 - 36 mL x kg x min) with a correspondingly wide range in accelerometer counts (1- 3928 counts x 30 s). The regression line for MET score versus counts was MET = 2.01 +/- 0.00171 (counts x 30 s) (mixed model R = 0.84, SEE = 1.36). A threshold of 1500 counts x 30 s defined the lower end of the moderate intensity (approximately 4.6 METs) range of physical activity. That cutpoint distinguished between slow and brisk walking, and gave the lowest number of false positive and false negative classifications. The threshold ranges for sedentary, light, moderate, and vigorous physical activity were found to be 0 - 50, 51- 1499, 1500 - 2600, and >2600 counts x 30 s, respectively. CONCLUSION The developed equation and these activity thresholds can be used for prediction of MET score from accelerometer counts and participation in various intensities of physical activity in adolescent girls.

Performance of Screening Mammography among Women with and without a First-Degree Relative with Breast Cancer

Many guidelines recommend that women at high risk for breast cancer undergo regular screening mammography at a younger age than those at average risk (1). However, few studies have evaluated the performance of screening mammography among younger women at increased risk for breast cancer. One group reported that the positive predictive value of mammography was two- to threefold higher (2) but the sensitivity was slightly lower (3) in women who had at least one first-degree relative with a history of breast cancer compared with those who did not. No randomized, controlled trials or subgroup analyses of data from existing randomized, controlled trials of screening mammography have evaluated the efficacy of the test in women with a family history of breast cancer. Understanding whether a family history of breast cancer influences the test performance characteristics of mammography may be important in developing screening strategies. This may be especially true for younger women, in whom the positive predictive value of mammography is low and the likelihood of associated diagnostic procedures to evaluate an abnormal result is high (2, 4, 5). We pooled data from seven mammography registries in order to provide a more stable estimate of the accuracy of screening mammography among women with a first-degree family history of breast cancer. We also compared the accuracy of the test in these women and in women of similar age without a family history. In this study, we report the rate of cancer, cancer yield per breast biopsy, and positive predictive value and sensitivity of mammography according to family history and decade of age. Methods Participants and Data Sources Our study sample included women 30 to 69 years of age who underwent screening mammography from April 1985 to November 1997. Data were pooled from seven mammography registries that participate in the National Cancer Institute Breast Cancer Surveillance Consortium (BCSC) (6). The seven registries, which are funded by the National Cancer Institute or the Department of Defense, are the San Francisco Mammography Registry (SFMR), San Francisco, California; Group Health Cooperative (GHC), Seattle, Washington; Fred Hutchinson Cancer Research Center (FHCRC), Seattle, Washington; New Mexico Mammography Project (NMMP), Albuquerque, New Mexico; Vermont Mammography Registry (VMR), Burlington, Vermont; Colorado Mammography Advocacy Project (CMAP), Denver, Colorado; and New Hampshire Mammography Network (NHMN), Hanover, New Hampshire. The SFMR provided data from April 1985 to December 1993, the GHC provided data from January 1986 to December 1993, the FHCRC provided data from December 1987 to December 1996, the NMMP provided data from June 1992 to December 1995, the VMR provided data from January 1994 to December 1996, the CMAP provided data from August 1994 to December 1996, and the NHMN provided data from May 1996 to November 1997. One mammographic examination per woman was included in the pooled analysis. If a woman had more than one mammographic examination in a mammography registry, results from her earliest dated examination were included and results from any subsequent screening examinations were excluded. We excluded women with a previous diagnosis of breast cancer and those with a palpable breast mass by history or on physical examination. Women whose ZIP codes were outside the catchment areas of their regional Surveillance, Epidemiology, and End Results (SEER) program or state tumor registry were also excluded to minimize incomplete follow-up information. The University of California, San Francisco, Committee on Human Research approved the study. Measurements We obtained a self-reported risk profile for breast cancer for each woman, as well as a mammographic assessment of two standard screening views per breast. The risk profile for breast cancer included questions about family history of breast cancer in a first-degree relative. Women were considered to have a family history of breast cancer if they reported having at least one first-degree relative (mother, sister, or daughter) with breast cancer. Results of initial screening examinations were classified as normal or abnormal. In mammography registries that used the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) (7) or terminology consistent with BI-RADS to assign mammographic assessment categories (SFMR, FHCRC, NMMP, VMR, NHMN, and CMAP), findings considered negative (category 1) or benign (category 2) were classified as normal. Examinations reported with any of the following BI-RADS assessments were categorized as abnormal: 1) probably benign (category 3); 2) incomplete, needs additional imaging evaluation (category 0), 3) suspicious (category 4), and 4) highly suggestive of malignancy (category 5). Before using BI-RADS, GHC used three mammographic assessment codes: negative, indeterminate, and positive. Negative and indeterminate assessments (for which follow-up in 1 year was recommended) were classified as normal; indeterminate assessments (for which 6-month follow-up examinations, additional imaging, or biopsy was recommended) and all positive assessments were classified as abnormal. Follow-up Breast biopsies performed to evaluate an abnormal mammography result were identified by contacting the womans personal physician, performing data linkage with a pathology database, or performing data linkage with a radiology database, depending on the study site. Breast biopsies included excisional and core biopsies. Women who had screening examinations were linked by computer to a pathology database (VMR, NHMN), to SEER (GHC, SFMR, NMMP, FHCRC), or to a state tumor registry (VMR, NHMN, CMAP) that collects population-based cancer data. To maintain participant confidentiality, procedures for linkage were performed according to protocols for human subjects research. Women were linked by using their full names, birth dates, addresses, ZIP codes, and Social Security numbers, when available, by using a probability-matching software program (Automatch, Vality Technology, Inc., Boston, Massachusetts) (VMR, NHMN, SFMR) or a comparable software program developed for linkage by a mammography registry (GHC, FHCRC, NMMP, CMAP). To allow adequate time for breast cancer to be reported to a tumor registry after a normal mammography result, we included only women who were screened through November 1997. Women were considered to have breast cancer if reports from a breast pathology database, SEER program, or state tumor registry showed any invasive carcinoma or ductal carcinoma in situ. Women with lobular carcinoma in situ only were excluded. Results for all cases of breast cancer and results for invasive cancer are presented separately. Definitions If breast cancer was diagnosed within 12 months of a normal mammography result, the examination was considered to be a false negative. If breast cancer was not diagnosed within 12 months of a normal mammography result, the examination was considered to be a true negative. If breast cancer was diagnosed within 12 months of an abnormal mammography result, the examination was considered to be a true positive. If breast cancer was not diagnosed within 12 months of an abnormal mammography result, the examination was considered to be a false positive. The diagnosis date was the date reported by a SEER program, the date reported by a state tumor registry, or the biopsy date recorded in a pathology database. Statistical Analysis The positive predictive value of screening mammography was calculated as the percentage of women with abnormal screening examinations who received a diagnosis of breast cancer within 12 months of the screening examination. Since the positive predictive value of mammography is influenced by the criteria used to define an examination as abnormal, we also reported the number of cases of breast cancer detected per 1000 screening examinations (normal and abnormal combined) when breast cancer was diagnosed within 1 year of the screening examination. The cancer yield per breast biopsy was calculated as the percentage of women who had a breast biopsy and received a diagnosis of breast cancer within 12 months of the screening examination. The sensitivity of mammography was calculated as the number of true-positive examinations divided by the number of true-positive examinations plus the number of false-negative examinations. The specificity of mammography was calculated as the number of true-negative examinations divided by the number of false-positive examinations plus the number of true-negative examinations. The chi-square test and the Fisher exact test were used for comparison of proportions. The chi-square test for trend and the chi-square test for homogeneity were used to compare proportions stratified by age. All Pvalues were two sided. Role of the Funding Sources The funding sources had no role in the collection, analysis, or interpretation of the data or in the decision to submit the paper for publication. Results A total of 389 533 screening examinations were performed among seven mammography registries. Of these, 50 834 (13.0%) were performed in women with a family history of breast cancer. Five registries record self-reported previous use of mammography. In data from these registries, previous use was similar among women with a family history of breast cancer (81.7% [28 574 of 34 973]) and among those without (80.2% [170 505 of 212 729]). Abnormal Mammography Results Among women without a family history of breast cancer, the overall frequency of abnormal examination results was 10.8% (95% CI, 10.7% to 11.0%). The frequency of abnormal results ranged from 8.8% to 11.3% across age groups and was lowest for women 30 to 39 years of age (Table 1). The frequency of abnormal examination results was higher among women with a family history of breast cancer than among those without (12.7% vs. 10.8%; P<0.001 [chi-square test]); these differe