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Moini A, Salari E, Rashidi H, Maajani K, Abedi M, Bayani L, Alipour S. Evaluation of the association of endometriosis and mammographic breast density, a cross-sectional study. BMC Womens Health 2022; 22:81. [PMID: 35313883 PMCID: PMC8935711 DOI: 10.1186/s12905-022-01663-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Background Endometriosis is a common benign but painful gynecologic condition. Studies suggest that the risk of some types of malignancies such as breast cancer is higher in women with endometriosis. Mammographic breast density (MBD) is known as an important predictor for breast cancer. The present study aimed to investigate the potential relationship between endometriosis and MBD. Methods This cross-sectional study was conducted on 370 women over 40 years of age. Laparoscopic surgery was carried out for the diagnosis of endometriosis. MBD was classified into four categories according to the ACR BI-RADS classification. Statistical analysis was performed using SPSS software to evaluate the potential association between variables. Results The mean age of all participants was 47.2 ± 6.4 years, and most participants (76.8%) were premenopausal. Multivariate analysis of the potential predictors of MBD, including age, body mass index, oral contraceptive consumption, progesterone consumption, family history of breast cancer and endometriosis showed that age (P value = 0.002), history of progesterone consumption (P value = 0.004) and endometriosis (P value = 0.006) were independent factors for MBD. Conclusion This study indicated that endometriosis had an inverse association with MBD. Age and history of progesterone use were also independent influential factors for MBD. This finding shows that the positive association between breast cancer and endometriosis is not mediated through MBD.
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Affiliation(s)
- Ashraf Moini
- Breast Disease Research Center (BDRC), Tehran University of Medical Sciences, Tehran, Iran.,Department of Surgery, Arash Women's Hospital, Tehran University of Medical Sciences, Shahid Baghdarnia (North Rashid) Street, Ressalat Street, 1653915911, Tehran, Iran.,Department of Endocrinology and Female Infertility, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
| | - Elnaz Salari
- Department of Surgery, Arash Women's Hospital, Tehran University of Medical Sciences, Shahid Baghdarnia (North Rashid) Street, Ressalat Street, 1653915911, Tehran, Iran
| | - Hadi Rashidi
- Breast Disease Research Center (BDRC), Tehran University of Medical Sciences, Tehran, Iran
| | - Khadije Maajani
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahboubeh Abedi
- Department of Radiology, Arash Women's Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Leila Bayani
- Department of Radiology, Arash Women's Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Sadaf Alipour
- Breast Disease Research Center (BDRC), Tehran University of Medical Sciences, Tehran, Iran. .,Department of Surgery, Arash Women's Hospital, Tehran University of Medical Sciences, Tehran, Iran.
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Spiegelman D, Khudyakov P, Wang M, Vanderweele TJ. Evaluating Public Health Interventions: 7. Let the Subject Matter Choose the Effect Measure: Ratio, Difference, or Something Else Entirely. Am J Public Health 2017; 108:73-76. [PMID: 29161073 DOI: 10.2105/ajph.2017.304105] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
We define measures of effect used in public health evaluations, which include the risk difference and the risk ratio, the population-attributable risk, years of life lost or gained, disability-adjusted life years, quality-adjusted life years, and the incremental cost-effectiveness ratio. Except for the risk ratio, all of these are absolute effect measures. For constructing externally generalizable absolute measures of effect when there is superior fit of the multiplicative model, we suggest using the multiplicative model to estimate relative risks, which will often be obtained in simple linear form with no interactions, and then converting these to the desired absolute measure. The externally generalizable absolute measure of effect can be obtained by suitably standardizing to the risk factor distribution of the population to which the results are to be generalized. External generalizability will often be compromised when absolute measures are computed from study populations with risk factor distributions different from those of the population to whom the results are to be generalized, even when these risk factors are not confounders of the intervention effect.
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Affiliation(s)
- Donna Spiegelman
- Donna Spiegelman is with the departments of Epidemiology, Biostatistics, Nutrition, and Global Health, Harvard T. H. Chan School of Public Health, Boston, MA. Donna Spiegelman and Molin Wang are also with the Channing Division of Network Medicine, Brigham and Women's Hospital, Boston. Polyna Khudyakov is with the Department of Epidemiology, Harvard T. H. Chan School of Public Health. Molin Wang is with the departments of Epidemiology, Biostatistics, and Medicine, Harvard T. H. Chan School of Public Health. Tyler J. Vanderweele is with the departments of Epidemiology and Biostatistics, Harvard T. H. Chan School of Public Health
| | - Polyna Khudyakov
- Donna Spiegelman is with the departments of Epidemiology, Biostatistics, Nutrition, and Global Health, Harvard T. H. Chan School of Public Health, Boston, MA. Donna Spiegelman and Molin Wang are also with the Channing Division of Network Medicine, Brigham and Women's Hospital, Boston. Polyna Khudyakov is with the Department of Epidemiology, Harvard T. H. Chan School of Public Health. Molin Wang is with the departments of Epidemiology, Biostatistics, and Medicine, Harvard T. H. Chan School of Public Health. Tyler J. Vanderweele is with the departments of Epidemiology and Biostatistics, Harvard T. H. Chan School of Public Health
| | - Molin Wang
- Donna Spiegelman is with the departments of Epidemiology, Biostatistics, Nutrition, and Global Health, Harvard T. H. Chan School of Public Health, Boston, MA. Donna Spiegelman and Molin Wang are also with the Channing Division of Network Medicine, Brigham and Women's Hospital, Boston. Polyna Khudyakov is with the Department of Epidemiology, Harvard T. H. Chan School of Public Health. Molin Wang is with the departments of Epidemiology, Biostatistics, and Medicine, Harvard T. H. Chan School of Public Health. Tyler J. Vanderweele is with the departments of Epidemiology and Biostatistics, Harvard T. H. Chan School of Public Health
| | - Tyler J Vanderweele
- Donna Spiegelman is with the departments of Epidemiology, Biostatistics, Nutrition, and Global Health, Harvard T. H. Chan School of Public Health, Boston, MA. Donna Spiegelman and Molin Wang are also with the Channing Division of Network Medicine, Brigham and Women's Hospital, Boston. Polyna Khudyakov is with the Department of Epidemiology, Harvard T. H. Chan School of Public Health. Molin Wang is with the departments of Epidemiology, Biostatistics, and Medicine, Harvard T. H. Chan School of Public Health. Tyler J. Vanderweele is with the departments of Epidemiology and Biostatistics, Harvard T. H. Chan School of Public Health
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