November 3, 2009 at Brigham and Women’s Hospital Offices, 14.15-17.00
THEMATIC WORKING GROUP SESSIONS (pre-registration required)
Evidence for an Emerging Breast Cancer Epidemic: Why the Big Numbers? Why the Younger Ages?
(previously called “Explaining Differences in the Age Distribution of Breast Cancer Across Countries ")
Co-leaders: Donna Spiegelman, Professor of Epidemiological Methods, Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health
Clement Adebamowo, Associate Professor, Department of Epidemiology and Preventive Medicine, School of Medicine and Institute of Human Virology, University of Maryland, Baltimore and Director, Office of Strategic Information and Research, Institute of Human Virology in Nigeria
Rapporteur: Elysia Álvarez, Harvard School of Public Health
Reading Materials
Spiegelman D, et al., Population Attributable Risk of Postmenopausal Breast Cancer
Due to Six Breast Cancer Risk Factors. Rough draft, October 2009.
Population Attributable Risk of Postmenopausal Breast Cancer
Due to Six Breast Cancer Risk Factors
Stephanie A. Smith-Warner, Ph.D.
Donna Spiegelman, Sc.D.
Shiaw-Shyuan Yaun, M.P.H.
Hans-Olov Adami, M.D.
Lawrence Beeson, M.S.P.H.
Piet A. van den Brandt, Ph.D.
Graham A. Colditz, M.D.
Aaron R. Folsom, M.D.
Gary E. Fraser, M.D.
R. Alexandra Goldbohm, Ph.D.
Anthony B. Miller, M.B., B.Ch.
John D. Potter, M.B., B.S., Ph.D.
Thomas E. Rohan, M.B., Ph.D.
Walter C. Willett, M.D.
Alicja Wolk, Dr.Med.Sci.
David J. Hunter, M.B., B.S.
Author affiliations:
· Harvard School of Public Health, Departments of Nutrition (S.A.S.-W., W.C.W.), Epidemiology (D.S., G.A.C., W.C.W., D.J.H.), Biostatistics (D.S.), Boston, MA
· Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA (W.C.W., G.A.C., S.-S.Y., D.J.H.)
· Harvard Center for Cancer Prevention, Boston, MA (W.C.W., G.A.C., D.J.H.)
· Department of Medical Epidemiology, Karolinska Institutet, Stockholm, Sweden (H.-O.A., A.W.)
· The Center for Health Research, Loma Linda University School of Medicine, Loma Linda, CA (L.B., G.E.F.)
· Department of Epidemiology, University of Maastricht, Maastricht, The Netherlands (P.A.B.)
· Division of Epidemiology, School of Public Health, University of Minnesota, Minneapolis, MN (A.R.F.)
· Department of Epidemiology, TNO Nutrition and Food Research Institute, Zeist, The Netherlands (R.A.G.)
· NCIC Epidemiology Unit, Department of Preventive Medicine and Biostatistics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada (A.B.M., T.E.R.)
· Cancer Prevention Research Program, Fred Hutchinson Cancer Research Center, Seattle, WA (J.D.P.)
Correspondence to: Stephanie A. Smith-Warner, Ph.D., Harvard School of Public Health, Department of Nutrition, 665 Huntington Avenue, Boston, MA 02115 (e-mail: ; phone: 617-432-4655; fax: 617-432-2435).
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Supported by research grants NIH CA55075 and CA50597, by the Wallace Genetic Foundation, Inc. and by the Cancer Research Foundation of America/American Society of Preventive Oncology research fellowship to Dr. Smith-Warner, and by a Faculty Research Award (FRA-455) to Dr. Hunter from the American Cancer Society.
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Abstract
Background: The population attributable risk (PAR) is the proportion of cases of a disease that would be avoided if a population experienced a shift in the distribution of causal risk factors to an overall lower risk level.
Methods: We calculated PARs for postmenopausal breast cancer for age at menarche, parity, age at first birth, body mass index and, height. Relative risks were estimated from the Pooling Project of Prospective Studies of Diet and Cancer; risk factor prevalences were estimated from the 1987 U.S. National Health Interview Survey, a 1989 survey in rural China, and from the studies comprising the Pooling Project.
Results: For these five risk factors, the PAR for a simultaneous change in the distributions observed in U.S. women to below the median values observed in women in rural China was 57% (95% CI 44-71%). Differences in the distribution of these five factors accounted for 35% (95% CI 25-45%) of the difference in breast cancer incidence rates between the U.S. and rural China. The estimated reduction in breast cancer risk for a simultaneous change in the median value of each factor to the median in rural China ranged from 37-44% across the countries represented by the Pooling Project studies.
Conclusions: We found that a moderate proportion of the difference in breast cancer incidence between China and the U.S. is accounted for by the five variables as we measured them. However, because these variables may be measured with considerable error, or are only indirect surrogates of the biologically relevant variables, our findings probably represent a serious underestimate of the contribution of these factors to international differences in breast cancer rates.
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INTRODUCTION
Breast cancer is a leading cause of cancer in women worldwide 1. However, breast cancer incidence and mortality rates vary considerably across countries. Rates tend to be higher in North America and Europe and lower in Asia and Africa 1. Breast cancer incidence rates also vary within countries. For example, the 1988-1992 breast cancer incidence rates were 90.7/100,000/year in the United States (U.S.), 26.5/100,000/year in Shanghai, one of the largest cities in China, and 11.2/100,000/year in Qidong, a more rural city near Shanghai 2. Migrant studies demonstrate that differences in lifestyle and reproductive factors contribute to the differences in incidence rates 3-5. However, the proportion of the international variation in breast cancer incidence that is explained by differences in breast cancer risk factors is uncertain.
The potential impact of risk factors on breast cancer occurrence can be evaluated by estimating the population attributable risk (PAR) due to individual risk factors and combinations of risk factors. The PAR is the proportion of cases that would be avoided if the risk factor distribution of a high risk population switched to that of a low risk population 6, 7. PARs may be used to compare disease incidence rates among countries with different risk factor distributions, to evaluate the impact of established risk factors on a disease within a population, and to examine the potential impact of interventions designed to decrease the risk of a disease 6. Calculation of PARs assumes that the variables examined are causally associated with the disease of interest. The most common risk factors that have been used to estimate PARs for breast cancer have been age at first birth, number of breast biopsies/history of benign breast disease, family history, and alcohol consumption 6, 8-15.
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We evaluated the proportion of postmenopausal breast cancer in the United States that theoretically could be avoided if the distributions for age at menarche parity, age at first birth, body mass index, and height for women living in the U.S. changed to the distributions seen among women living in rural China. In addition, we estimated the change in breast cancer risk associated with a shift in the median values of these five risk factors for each of the four high-risk countries represented in the Pooling Project of Prospective Studies of Diet and Cancer (the U.S., Canada, the Netherlands, and Sweden) to the median values of a lower-risk country (China). We focused on postmenopausal breast cancer only because most breast cancer occurs among postmenopausal women 16, some risk factors differ for premenopausal and postmenopausal breast cancer 17-20 and because only four cohorts in the Pooling Project included premenopausal participants, resulting in limited power for analyses of premenopausal breast cancer. We chose China as an example of a lower-risk country because information on these risk factors at an individual level was available from a national survey (Dr. Banoo Parpia, personal communication). The five risk factors were selected because they have been consistently associated with breast cancer risk in epidemiologic studies 17, 21-23 and there is biological evidence that these risk factors are causal 24, 25. Although alcohol consumption has been shown to increase the risk of breast cancer 12, 22, we did not include alcohol consumption in these analyses because the median alcohol consumption was similar between women living in rural China and the U.S. However, including alcohol consumption in our analyses probably would not have materially changed the results because the PAR for switching from any alcohol consumption to none has been estimated as 2% in the United States 26.
METHODS
In these analyses, we used the PAR as an estimate of the excess fraction, the proportion of cases that would not have occurred if the exposure of interest had not been present 27. We estimated the PAR for postmenopausal breast cancer associated with five breast cancer risk factors: age at menarche, parity, age at first birth, body mass index, and height. We used the term “summary PAR” to refer to a PAR calculated for the combination of one or more risk factors.
The full PAR (PARF), estimated the proportion of breast cancer cases that would be avoided if the distribution of each of the risk factors in the model was switched to its corresponding low-risk category. The PARF was computed as
where RRs is the multivariate-adjusted relative risk determined from the Pooling Project 28 and ps is the U.S. population prevalence obtained from the 1987 U.S. National Health Interview Survey (NHIS) 29 for the sth combination of levels of the six risk factors, s=1,...,S 30. The variance for the estimate of PARF using external population prevalences is derived in Appendix 1.
The partial PAR (PARP) evaluated the percent reduction expected in the crude breast cancer incidence rate of the target population if some, but not all, of the risk factors in the model were eliminated from the target population 6. Appendix 2 shows the derivation of the formula for estimating the PARP when the relative risks and prevalences are estimated in different populations. The corresponding variance for the PARp is derived in Appendix 3.
Because PAR estimates obtained using multiple exposure categories are roughly equivalent to estimates obtained using a single dichotomous exposure category grouped over these multiple categories 31, age at menarche, body mass index, and height, were each defined as categorical variables with two levels. Parity and age at first birth were modelled jointly as an interaction term with five levels. The reference group for each variable was defined as the group with the lowest risk. The cutpoint for each risk factor was chosen using the median level observed in a 1989 communication, 1989 Chinese ecologic survey of 69 rural countries in China 32.
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To estimate the proportion of breast cancer cases in the U.S. that would be avoided if the distributions of age at menarche, parity, age at first birth, body mass index, and height in women living in the U.S. were changed to the distributions of women living in rural China (rather than to the low-risk category which results in a larger change in the risk factor distribution), we calculated the 2-country PAR (2C-PARF). The derivation of the 2C-PARF and its variance are included in Appendix 4. For these analyses, the joint distribution of the risk factors was estimated using the 1987 U.S. NHIS (described below) and a 1989 Chinese ecologic survey of 69 rural counties in China 32 (Dr. Banoo Parpia, personal communication).
The relative attributable risk (RAR) 33 was used to estimate the proportion of the difference between the Chinese and U.S. breast cancer incidence rates that was due to differences in the distributions of these five risk factors between the two countries. The RAR was estimated as
where IL is the incidence rate in China (the low-risk population) and IH is the incidence rate in the U.S. (the high-risk population). The 95% confidence interval was estimated using the multivariate delta method 34.
We also estimated the percent reduction in breast cancer risk associated with a shift in the median value in each risk factor in four high-risk countries (the U.S., Canada, Netherlands, and Sweden) compared to the median value in a low-risk country (China) 35. The median value for each risk factor was estimated using the 1987 U.S. NHIS 29, the sub-cohort of the Canadian National Breast Screening Study 36, the sub-cohort in the Netherlands Cohort Study 37, and the baseline population of the Sweden Mammography Cohort 38. Participants in the three cohort studies were recruited from population registries 37 or mammography screening clinics 36, 38. The risk factor distribution in rural China was estimated from a 1989 Chinese ecologic survey of 69 rural counties in China 32 (Banoo Parpia, personal communication). The relative risks of developing breast cancer were estimated from the Pooling Project. The risk factors were modelled as continuous variables in the regression analyses with linearity assumed; nulliparous women were assigned a value of zero for age at first birth. Sensitivity analyses were conducted using the lower and upper 95% CI bounds for each risk factor 39.
Pooling Project
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For calculating the PAR, we estimated the relative risks for postmenopausal, invasive breast cancer from the Pooling Project 23, 28, 40. Briefly, seven prospective studies 37, 38, 41-45 (Table 1) were identified that met the following pre-defined criteria: 1) identified at least 200 incident cases of breast cancer; 2) assessed usual intake of foods and nutrients, and 3) completed a validation study of the dietary assessment instrument or closely related instrument. The New York State Cohort was excluded from these analyses because age at menarche was not ascertained in that study. The Nurses' Health Study was divided into two studies (1980-1986 and 1986-1995 follow-up periods) because it had repeated assessments of breast cancer risk factors and a longer follow-up period than the other studies. Self-administered questionnaires were used to assess reproductive factors, anthropometric factors, diet, medical history, and family history in each study. Incident breast cancers were ascertained using follow-up questionnaires, inspection of medical records and/or tumor-registry linkage. In all cohorts, follow-up was estimated to be more than 90 percent complete.
For each study, after applying the exclusion criteria used by that study, we excluded participants who reported energy intakes greater or less than three standard deviations from the study-specific loge-transformed mean energy intake of the baseline population, reported a history of cancer except non-melanoma skin cancer at baseline, or were premenopausal at baseline. Participants also were excluded from these analyses if they had missing data on age at menarche, parity, age at first birth, body weight, or height.