Breast cancer is the most common cancer in New Zealand, with approximately 3000 new registrations annually, affecting one in nine women and resulting in more than 600 deaths. This study analyzed data of patients with selected prognostic factors of Nottingham grade 3 tumors over a specified five-year period. The study aimed to identify factors that result in differential survival in the female, New Zealand population.This is an observational, retrospective cohort study of prospectively collected data from New Zealand Breast Cancer Register. The selected period of 1st January 2011 to 31st, December 2015 allowed a consistent overlap for a national five-year data of grade 3 breast cancer in New Zealand. Mortality was carried out using univariate Fine-Gray competing risk statistical models.p = 0.053). Hormonal receptor analysis showed that ER positive, PR negative, and ER negative, PR negative subjects were at increased risk  and  respectively. Molecular subtypes Triple Negative Breast Cancer and Luminal B subjects were at increased risk  respectively. HER2 enriched subjects were at a higher, but not significant, risk of five-year mortality compared to luminal A . NZ Europeans were at increased risk , with the highest Cumulative Incidence Function CIF, the largest proportion of HER2 enriched and TNBC across ethnicities.; however, Pacific Islanders experienced the highest HER2 CIF.This study showed that women in the older age group (> 70 years) had higher five-year mortality risk (HR: 1.7, 95% CI: 0.9–3.0, The survival rates for grade 3 breast cancer vary across the selected prognostic factors and ethnicity. The results of this study make an initial contribution to the understanding of grade 3 breast cancer in the New Zealand population. Breast cancer is the most frequently diagnosed cancer for women worldwide and the disease has a considerable impact on our society , 2. New Epithelial breast tumors are graded according to the Nottingham criteria  outside of the Pacific Islands makes it a unique population. However, the largest ethnic group by far are Europeans followed by Maori –10.Within this diverse ethnicity, breast cancer can be assessed for variable presentation, biology, and survival. This provides a comparison with global literature and a unique report on breast cancer mortality using the New Zealand Breast Cancer Register (NZBCR).The results should make an initial contribution to the understanding of this selected heterogenous high-grade group.This is an observational, retrospective cohort study of prospectively collected data, that aims to investigate by the audit of evaluating selected prognostic factors of breast cancer survival of women diagnosed with grade 3 breast cancer in New Zealand over a selected period. No other grade of breast cancer formed part of this study group. Data from the NZBCR for grade 3 breast cancer were analyzed in an attempt to stratify its impact in New Zealand. The period selected was from 1st January 2011 to 31st December 2015, which allowed a consistent overlap of data from all four registers , Waikato, Wellington , and Christchurch , as shown in Fig. Sources of data for NZBCR data include pathology laboratories, oncology services, and the breast cancer registry . The oveTo subtype ER positive tumours: PR status, HER2 status and the Ki67 count are utilised in classifying Luminal A and Luminal B. Currently there is no consensus agreement on Ki67 reporting and cut off values amongst the New Zealand pathologists. There is inconsistent reporting in labs within the regions and nationally, resulting in incomplete data in the register, to use as a criterion, so for this study, the definition of Luminal B subtypes had to be based on hormone receptor and HER2 results.Age at diagnosis was divided into seven groups: 20–40 years (due to the low numbers this was grouped as group 1), 41–50 years (group 2), 51–60 years (group 3), 61–70 years (group 4), 71–80 years (group 5), and > 80 (group 6). Ethnicity data were grouped into 5 categories: Māori, Pacific Islander, NZ European, Asian, and Other. Due to low numbers, the “Other” group was combined with “Asian”.Descriptive characterization of the study population was carried out using standard, frequency, and percentage summarization. The NZBCR characterized cause of death as breast cancer related or other causes, so univariate Fine-Gray competing risk models were carried out, instead of the traditional Cox proportional hazard regression, with Hazard Ratios (HR) reported. The reference group selected for each dependent variable was the most favorable prognostic factor outcome in each group. Cumulative Incidence Factors (CIF) are reported, as competing risk models were run. Multivariate and interaction models were not able to be run due to small subgroup cell counts. All analyses were carried out using Statistical Data Analysis (STATA) version 16.0.p = 0.06). Women greater than 70 years were at elevated risk .; women aged 81 or greater at significantly increased risk  of breast cancer-related causes. The NZ European population was at elevated risk . In contrast, the Māori and Pacific Islander populations were not at elevated risk of five-year mortality  and  respectively.Over the five-year study period from 1st January 2011 to 31st December 2015, 2493, women were diagnosed with grade 3 breast cancer, the NZBCR recorded a breast cancer fatality rate of 42.9%. The cohort’s overall demographics are summarised in Table p = 0.10) when compared to luminal A and TNBC that were higher and significant risk of five-year mortality .Ductal carcinoma represented the largest proportion of grade 3 cancers Table . There wp = 0.00) and ER−/PR- . Of note, the Pacific Islander group represented more ER/PR positive tumors whereas the Māori group represented more ER/PR negative  subjects were at increased risk of five-year mortality. The latter is consistent with literature findings –30 whereLuminal B subtype has a poorer prognosis and women with PR-negative combinations  were at increased risk of five-year mortality and this is consistent with literature finding  where thStudies describing equity-focussed improvements in health care may have improved the survival disparity between Māori and NZ European women , 32, 33.From the analysis, we found that survival rates for breast cancer varied across the selected prognostic factors and confirms that it is dependent on multiple factors. Previous studies have shown that some of these include: patient factors, tumor biology, and ethnicity, as well as access to health interventions and treatment, socioeconomic status, availability of drugs, and the treatment type available –37. ThesBased on the literature and results, to help understand survival disparities within the ethnic groups, in particular Maori and Pacific Islander, the following recommendations/suggestions should be considered: understanding tumor biology and genetic susceptibility of grade 3 breast cancer, understanding of patient risk profile, understanding how better access to service impacts on the outcome, quality and timeliness of care for patients, understanding mortality with family history, benefits of personalized care.Further analysis of the prognostic factors that were not included in this study such as lymph-vascular invasion (LVI), height, weight, biomarker (FISH) studies, number of nodes removed, type of surgery, type of treatment, and loco-regional recurrence status should be included.These factors may each independently and/or collaboratively influence survival and could help to further categorize grade 3 breast cancer in NZ women.This is the first retrospective study using prospectively collected data from the NZBCR to analyze grade 3 breast cancers in NZ as a stand-alone grade, representing a study strength.The registry represents 67% of all breast cancer diagnosed in New Zealand). Data analysis was used to assess the impact of the disease in New Zealand with an emphasis on grade 3 breast cancer. The data is linked to census data to allow researchers to investigate the outcome of breast cancer across the different regions. The study also lies within the population-based nature of the breast cancer registry and the outcome (death) is linked via the patient record. Furthermore, the various ethnic groups present in the four regions are included reflective of the population of New Zealand.The study makes an initial contribution to the understanding of high-grade malignancy and has broad application in understanding survival rates of women with grade 3 breast cancer in New Zealand. This including other prognostic factors will give a better understanding of the survival differences with this high-grade malignancy.The data that we used also has some limitations. The histologic categorizations used that was based on diagnoses made by multiple pathologists in multiple institutions. The diagnostic criteria may vary somewhat by both individual pathologists and establishment, resulting in a certain degree of misclassification error.Other limitations in our data were mainly in the form of missing values. There is dependence on the data administrators to enter all data accurately and consistently on time. The data standardization for all four registries in NZ took effect from 2017 making future studies using the NZBCR more efficient. The data standardization will allow for missing data to be taken into consideration and will enable us to include all this missing information in an appropriate way to improve the overall picture for future studies using the NZBCR.There is limited literature on breast cancer for Pacific Island women. Studies thus far have reported that Pacific Island women have lower breast cancer incidence but higher mortality risk than Māori and European women in New Zealand , which aThe inconsistent recording of disease stage at diagnosis and treatment data may pose significant limitations for researching the causes of inequities . Whilst The histologic type of breast cancer in this group was assigned by multiple pathologists in multiple institutions. Although standardized guidelines from the Nottingham grading system are used, there is subjective variation, therefore the diagnostic criteria may vary somewhat by both individual pathologists and establishment, resulting in a certain degree of misclassification error.Furthermore, this study did not include data on health insurance status or lifestyle factors  breast density, or genetic testing, all of which can influence breast cancer outcome since these were not available from the registries –41.Finally, whilst studies have reported on ethnic inequities in breast cancer outcomes in New Zealand, there is insufficient data to fully understand the underlying causes of these differences , 37, 42.Grade 3 breast cancer is referred to as heterogeneous and high-grade cancer. Despite the high-grade cancer, we found that five-year survival varies by a combination of biological and ethnicity factors. Women in the “Asian” ethnicity group with luminal A subtype, presented with the best prognosis. The New Zealand Europeans, Maori and the Pacific Islanders are at increased risk of early death. Pacific Islanders with Luminal A or TNBC are at the greatest risk. The trajectory towards poor overall survival for Pacific Islanders needs more research to identify the causes of the survival disparity. A multitude of other factors may each independently or collaboratively also influence survival. Elucidation of these factors may help to further categorize grade 3 breast cancer and contribute to a greater understanding of the risk factors of grade 3 breast cancer in NZ, and possibly enable better outcomes.


Is the interaction between mammographic breast density and overweight or obesity associated with breast cancer risk, and if so, does any association vary according to menopausal status?In a cohort study of 3 248 941 premenopausal and 4 373 473 postmenopausal women aged 40 years or older screened for breast cancer, a positive additive interaction between high breast density and high body mass index was associated with an increased risk of breast cancer, especially among women in the postmenopausal period.This study suggests that women with overweight or obesity and dense breast tissue might benefit from tailored breast cancer screening strategies; these 2 factors should be incorporated into risk stratification in population-based breast cancer screening. Evidence suggests that breast density and body mass index (BMI) are strong breast cancer risk factors; however, their interactive associations are unknown. Elucidation of these interactive associations may help to increase understanding of the causes of breast cancer and find effective interventions for susceptible subgroups.To explore the association of the interaction of mammographic breast density and BMI with breast cancer risks among premenopausal and postmenopausal women.This prospective observational cohort study used population-based data of the Korean National Cancer Screening Program embedded in the National Health Insurance Service database to evaluate the breast cancer risk of 3 248 941 premenopausal cancer-free women and 4 373 473 postmenopausal cancer-free women aged 40 years or older who underwent mammographic screening between January 1, 2009, and December 31, 2013, and were followed up until December 31, 2018. Statistical analysis was performed from June 1 to July 15, 2021.Breast Imaging Reporting and Data System (BI-RADS)–defined breast density  and BMI levels classified according to the World Health Organization Asia-Pacific Region classification.Adjusted relative risk (aRR) of breast cancer during the follow-up period and interactions in additive and multiplicative scales. The study end point was the development of breast cancer.Of 3 248 941 premenopausal women  and 4 373 473 postmenopausal women  aged 40 years or older, 34 466 cases of breast cancer were identified among the premenopausal women, and 30 816 cases of breast cancer were identified among the postmenopausal women. Increased breast density was associated with an increased risk of breast cancer in both premenopausal and postmenopausal women across the BMI categories. Among premenopausal women, those in BI-RADS category 4 had an approximately 2-fold higher risk of breast cancer irrespective of BMI  than those with underweight status and in BI-RADS category 1. However, an association between BMI and the risk of breast cancer was found only in the postmenopausal women in all breast density categories compared with underweight women with BI-RADS category 1 . When the combined associations of breast density and BMI with the risk of breast cancer were considered, a high breast density and high BMI had a significant positive interaction on the additive scale for both premenopausal and postmenopausal women, especially the latter .This study suggests that breast density and BMI interact synergistically to augment breast cancer risk, with a stronger association found among postmenopausal women. Both factors should be incorporated into risk stratification in a population-based screening for public health significance. Women with overweight or obesity and dense breast tissue might benefit from tailored early screening strategies to detect breast cancer. This cohort study uses data from the Korean National Cancer Screening Program to evaluate the association of the interaction of mammographic breast density and body mass index with breast cancer risks among premenopausal and postmenopausal women. A high BMI had no association or was associated with a reduced breast cancer risk,4 whereas increased adiposity was positively associated with increased postmenopausal breast cancer.5Female breast cancer is the most commonly diagnosed cancer, accounting for 11.7% of all new cases of cancer in 2020.9 In particular, whether the combined association of breast density and BMI with breast cancer risk varies according to menopausal status has yet to be comprehensively examined, to our knowledge. Some findings suggest that women with overweight or obesity and dense breast tissue may have a high risk of breast cancer regardless of menopausal status.12 However, 1 pooled analysis from 4 case-control studies reported minimal modification by BMI of the associations of breast density with breast cancer risk.13Although dense breast tissue and obesity were individually proven to be strong risk factors for breast cancer, controversy exists regarding the magnitude of the combined associations of these 2 factors with breast cancer risk, in addition to heterogeneity of the associations according to menopausal status.Mammographic breast density and BMI could be measured during health examinations, and evaluating their joint associations could provide information on future breast cancer risk during screening and consequently help identify women at an elevated risk of breast cancer. Thus, this study aimed to explore the interaction of breast density and BMI and risk of breast cancer according to menopausal status.14 In Korea, the NHIS provides free biennial mammography breast cancer screening for women aged 40 years or older. Ascertainment of incident breast cancer cases was obtained by linking the database to the medical use database of the NHIS. Breast cancer cases were defined by the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes for invasive breast cancer (C50.0-C50.9) and ductal carcinoma in situ (D05.0-D05.9), in combination with the catastrophic illness code.15 Screening results and questionnaire data were transferred to the NHIS database after obtaining written informed consent from the women. Our study was approved by the institutional review board of the Hanyang University College of Medicine. In addition, permission to use the NHIS database was approved by the National Health Insurance Sharing Service system, and deidentified data were available to the researchers. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.In this cohort study, we used data from the Korean National Cancer Screening Program embedded in the National Health Insurance Service (NHIS) database.We used data of women who underwent mammography screening between January 1, 2009, and December 31, 2013. Information on breast cancer incidence was followed up until December 31, 2018. When a woman underwent mammographic screening more than once, information on the first screening was used. Among 8 116 653 women who underwent screening for breast cancer between 2009 and 2013, those who underwent screening at younger than 40 years of age, those with any cancer history before the date of mammographic screening, or those with missing information on menopausal status, BMI, or breast density were excluded. In addition, participants who received a diagnosis of any type of cancer or died within 180 days after breast cancer screening were excluded to prevent possible inclusion of prevalent cancer cases; the remaining 7 622 414 women were composed of 3 248 941 premenopausal and 4 373 473 postmenopausal women at baseline .Mammographic breast density was evaluated using the Breast Imaging Reporting and Data System (BI-RADS) classification of the American College of Radiology. The BI-RADS breast density was read by the radiologist at each screening center. The BI-RADS classification has been used to categorize breast density in the Korean National Cancer Screening Program since 2009. The BI-RADS mammographic breast density comprises 4 categories: 1 indicates almost entirely fat, 2 indicates scattered fibroglandular densities, 3 indicates heterogeneously dense tissue, and 4 indicates extremely dense tissue.16 Body mass index was categorized into the following 4 groups: underweight (<18.5), normal weight (18.5 to <23.0), overweight (23.0 to <27.5), and obese (≥27.5).During health examinations, height and weight were measured by trained nurses and used to calculate BMI. This study used the BMI cutoff points according to the World Health Organization Asia-Pacific classification.Information on other covariates was collected using standardized questionnaires during health examination and cancer screening. The questionnaires were distributed by nurses and self-reported by participants. The questionnaires measured information regarding health behaviors, including drinking, smoking, physical activity, family history of cancer and other major chronic diseases, and the reproductive physiological processes of all of the participants. In our analysis, we considered the following factors as covariates: participants’ age at screening, history of smoking, number of days of alcohol consumption per week, family history of breast cancer among first-degree relatives, history of benign breast disease, age at menarche, number of children, duration of breastfeeding, history of breastfeeding, use of oral contraceptives, and history of hormone replacement therapy.Statistical analysis was performed from June 1 to July 15, 2021. The study end point was the development of breast cancer. The association among BMI levels, BI-RADS breast density categories, and breast cancer risk was presented in terms of relative risks (RRs) using Poisson regression models, considering the rare events of breast cancer. The associations between BMI and breast cancer risk according to BI-RADS density classification and between BI-RADS density classification and breast cancer risk according to BMI were also evaluated. In addition, the combined associations of BMI and the BI-RADS density classification with breast cancer risk were presented using a Poisson regression model. All analyses were stratified by menopausal status and adjusted for the covariates as mentioned.RR = RR11 − RR10 − RR01 + 1. An RERI value of 0 indicated no additive interaction, a positive value indicated a super-additive or positive interaction, and a negative value indicated a subadditive (negative) interaction. The multiplicative interactions were assessed using the ratio of RRs: RR11/(RR10 × RR01). A multiplicative interaction value of more than 1 indicated a positive interaction, while a multiplicative interaction value of less than 1 indicated a negative interaction.17 Additive interaction is present when the combined association of BMI and breast density is larger than the sum of the individual associations. Multiplicative interaction was estimated to evaluate whether the combined association of BMI and breast density with breast cancer risk was larger than the product estimated association of BMI alone and breast density alone.17The interactions between breast density classification and BMI on the additive and multiplicative scales were examined in association with breast cancer risk. The additive interactions were assessed using relative excess risk due to interaction (RERI), which was computed as RERIP < .05 was considered statistically significant. The analyses were performed using SAS, version 9.4 (SAS Institute Inc).A 2-sided P < .001).In this cohort of 3 248 941 premenopausal cancer-free women  and 4 373 473 postmenopausal cancer-free women , 34 466 premenopausal women and 30 816 postmenopausal women subsequently received a diagnosis of breast cancer . The proIncreased breast density was associated with an increased risk of breast cancer for both premenopausal and postmenopausal women in total and across 4 BMI categories . Among pAmong premenopausal women, no significant association between BMI and the risk of breast cancer was found in any of the BI-RADS categories after adjusting for other covariates . HoweverAmong premenopausal women, those in BI-RADS categories 1 and 2 did not show significant associations with breast cancer risk, irrespective of obesity status, compared with those in BI-RADS category 1 who were in the underweight category . HoweverCompared with postmenopausal women in BI-RADS category 1 who were in the underweight category, women in BI-RADS category 1 with obesity  and women in BI-RADS category 4 who were in the underweight category  showed an increased risk of breast cancer ; this fiA high breast density and high BMI had a significant positive interaction on the additive scale in both premenopausal and postmenopausal women, especially the latter . The adj18 and for Korean women.19 Although increased adiposity was not associated with premenopausal breast cancer risk, it showed a strong association with an increased risk of postmenopausal breast cancer, which was also found in our study.21This population-based cohort study found that breast density was independently associated with breast cancer risk for premenopausal and postmenopausal women; however, obesity was associated with breast cancer risk only for postmenopausal women. Considering the combined associations of breast density and obesity, women with obesity in BI-RADS category 4 showed the highest risk of breast cancer among both premenopausal and postmenopausal women, which was 2-fold higher for premenopausal women and 6-fold higher for postmenopausal women than that for women in BI-RADS category 1 who were in the underweight category. A positive synergistic association in terms of additive interaction was suggested in both groups, especially for postmenopausal women. This study confirmed the association between dense breast tissue and increased breast cancer risk previously found in other populations22 Other case-control studies in the US12 and Japan23 also reported that breast density was associated with an increased risk of premenopausal and postmenopausal breast cancers, and the association was strong in women with overweight or obesity, showing a multiplicative interaction. In contrast, no multiplicative interaction association between breast density and BMI with breast cancer risk was reported in a pooled analysis of 4 case-control studies6 and a study on postmenopausal women from the Nurses’ Health Study.8 A recent nested case-control study reported that the combination of overweight or obesity and elevated breast density was associated with a higher risk of premenopausal breast cancer.9 In particular, a high association was found with estrogen receptor–negative subtypes ; however, no interactions between breast density and BMI were detected in postmenopausal women. Although the interactive associations of breast density and BMI with breast cancer risk have been studied previously, most studies reported only multiplicative interactions despite the necessity of assessing interactions in both multiplicative and additive scales.25 In our study, we assessed both scale interactions and identified that, despite the small or nonsignificant multiplicative interaction of BMI and breast density with breast cancer risk, positive interactions were observed in both premenopausal and postmenopausal women. Considering that the additive interaction model is a relevant public health measure,25 both breast density and adiposity might need to be considered in the identification of high-risk individuals in a population-based screening setting.Previous studies on the interactive associations between breast density and BMI with breast cancer risk provide mixed evidence. A case-control study found that women with a breast density of 76% to 100% had a 5-fold higher risk of breast cancer than those with a breast density of 0% to 10%; moreover, breast density had a significant multiplicative interaction with BMI.27 Thus, the biological mechanisms underlying the interaction between breast density and BMI with breast cancer risk are complex and poorly understood. Breast cancer growth involves neoplastic transformation of breast epithelial cells into malignant cells. Given that epithelial cells are surrounded by adipose tissue, inflammation in breast adipose tissue is thought to play an important role in breast tumor development. The inflammatory effects of adiposity are mediated through increases in breast density, which may explain the combined associations of BMI and breast density.28 Another mechanism is associated with the aromatase activity of body fat, which is a significant source of endogenous estrogens that promote breast tissue proliferation.26 These complex biological associations of adiposity and breast density may explain the different interaction measures in terms of additive or multiplicative scales. Studies have also reported that the absence of interaction on either scale would be common25; however, there would always be an interaction on at least 1 scale when both exposures were associated with breast cancer risk with sufficient statistical power.Percentage of breast tissue density and adiposity are individually associated with an increased risk of breast cancer but are negatively associated with one another.17 the positive additive interaction for both premenopausal and postmenopausal women found in our study might be informative. To our knowledge, this study is the first to comprehensively investigate the combined association of breast density and BMI with breast cancer risk among women in Asian countries based on population-based breast screening settings, considering both additive and multiplicative interactions. Thus, this study provides evidence from real-world data on breast cancer risk, and the findings are generalizable to East Asian women, who tend to have a higher breast density than Western women.29 In addition, given the consistent findings from this study with previous studies conducted in Western countries,12 our results could also be applied to Western populations of women with increasing obesity.This study has some strengths, including its large national cohort design and prospective ascertainment of breast cancer development. Most previous studies have investigated the interaction between BMI and breast density using a multiplicative approach. However, considering that additive interactions would be relevant for assessing the public health effect of an interaction,30 Interradiologist variability was assessed in randomly selected digital mammograms from the Korean National Breast Cancer Screening Program, which reported an interradiologist variability of 0.83, indicating very high agreement.31 Although information on the reliability of the BI-RADS density categories between radiologists in Korea remained limited, several previous studies showed high agreement in assessing the film BI-RADs density categories between radiologists.33 Another limitation of this study was the relatively small numbers of breast cancer cases in underweight women in BI-RADS density category 1 in both premenopausal women (31 cases among 4353 participants) and postmenopausal women (55 cases among 28 167 participants).This study also has some limitations. It included a population of women aged 40 years or older, which prevented us from examining breast cancer risk in younger groups. Furthermore, breast density was subjectively interpreted. Although the BI-RADS classification has been widely used for breast density classification, the results might vary depending on the ability and experience of radiologists. However, in Korea, a mammography education program to standardize the performance of radiologists is available and thus might increase the reproducibility of the density measures.35 Our findings suggest that breast density notification should be provided not as a stand-alone risk factor but as an adjunct factor with BMI for risk stratification in population-based mammographic screening settings for public health significance.This study found that elevated breast density and having overweight or obesity are independently associated with an increased risk of breast cancer and interact synergistically to augment breast cancer risk for both premenopausal and postmenopausal women in terms of additive interactions. In addition, strong multiplicative interaction associations of BMI and breast density with breast cancer risk were found for postmenopausal women. Recent work suggests that notification of breast density should be combined with breast cancer risk to identify women at the highest risk so that they can undergo supplemental imaging screening and engage in discussions with their health care professionals.


The notion of candidacy emerged three decades ago through Davison and colleagues’ exploration of people’s understanding of the causes of coronary heart disease. Candidacy was a mechanism to estimate one’s own or others risk of disease informed by their lay epidemiology. It could predict who would develop illness or explain why someone succumbed to it. Candidacy’s predictive ability, however, was fallible, and it was from this perspective that the public’s reticence to adhere to prevention messages could be explained, as ultimately anybody could be ‘at-risk’. This work continues to resonate in health research, with over 700 citations of Davison’s Candidacy paper. Less explored however, is the candidacy framework in its entirety in other illness spheres, where prevention efforts could potentially impact health outcomes. This paper revisits the candidacy framework to reconsider it use within prevention. In doing so, candidacy within coronary heart disease, suicide prevention, diabetes, and cancer will be examined, and key components of candidacy and how people negotiate their candidacy within differing disease contexts will be uncovered. The applicability of candidacy to address modifiable breast cancer risk factors or cancer prevention more broadly will be considered, as will the implications for public health policy. Cancer is a leading cause of death in Australia and around the world ,2 and, dAppropriate and effective action on primary prevention strategies is required to address cancer incidence, given that the World Health Organization estimates that 30–50% of cancers could be prevented through addressing behavioural, infectious and environmental risk factors . BehavioSecondary prevention strategies, such as screening, have been a pillar of cancer control, contributing to increasing early detection of cancer and resulting in positive impacts on survival rates ,14. WhisIn contrast, research by Meyer et al.  suggestsFurther impediment to primary prevention is a lack of awareness of, or confusion about what are the modifiable risk factors for cancer. While research demonstrates that people readily identify modifiable cancer risk factors made more obvious by environmental and physical changes, such as tobacco and asbestos with lung disease and sun exposure with skin cancer, other modifiable risk factors such as consuming alcohol are less easily recognised .In a survey of Australian adults, people reported feeling uncertainty about the link between alcohol and cancer; and 23% of those reporting they knew about the link, reported a belief that red wine had no negative effect on cancer risk, with 25% reporting they thought red wine decreased cancer risk . To clarPublic confusion about risk factors, is also apparent when considering breast cancer specifically. A study of women with no previous breast cancer diagnosis found the majority of women believed that breast cancer was related only to genetic or familial factors . Whilst Given the discrepancies between lay understandings and scientific evidence ,32,33, wCandidacy, an individual’s estimation of disease risk, informed by their ‘lay epidemiology’  may offeThree decades ago the seminal work of Davison et al.  introducThe aim of this paper is therefore to present the key elements of the candidacy framework, and its application to date within CHD, suicide prevention, diabetes, and cancer. In doing so we will advance our understanding of candidacy with the purpose of cultivating ideas of what the candidacy system might mean for future cancer prevention research and its potential for public health interventions more generally. With regard to breast cancer, understanding what informs women’s lay perceptions of breast cancer risk and how these shape their acknowledgement (or not) of candidacy are important considerations to understand women’s willingness to address modifiable risk factors, such as alcohol consumption to reduce their breast cancer risk. To date breast cancer candidacy has not been explored from this perspective. Reconsidering the novel approach of candidacy in the context of primary cancer prevention may offer new understandings that can inform public health policy to increase women’s knowledge of and action toward reducing modifiable risk factors at individual levels that have population/ community level impact on cancer reduction.Given a central tenet of candidacy is an assessment of disease risk, being cognisant of the broader risk literature and the differing perspectives on risk can and should be explored. Taylor-Gooby and Zinn  provide With particular reference to understanding ‘cancer candidacy’, Zinn  has callWe live in a world where risk is knitted into the fabric of our existence. Information about risk is everywhere—and is readily available to shape our perceptions of risk (at the click of a mouse or the press of a touchscreen on a smart phone). In the contemporary neo-liberal society within which individuals are urged to be self-governing and show responsibility for their health and outcomes, and alongside an almost endless amount of information on risks, there is the potential to cause ‘existential anxiety’  or even Therefore, a key question is how should public health interventions position cancer risk that will resonate with the population in a meaningful way? This is particularly important for alcohol as a modifiable cancer risk, given the high global consumption of alcohol, where even low levels of consumption are linked to cancer risk, especially in the case of breast cancer . The impDavison’s notion of candidacy  emerged Candidacy emerged as an explanatory framework for CHD that could predict the risk of illness or death in others from CHD or assess one’s own risk, and retrospectively explain other people’s illness/death from CHD, or one’s own illness. A ‘type’ of person was identified as a CHD candidate based on their physical appearance , their social context  or their personal characteristics . Through the process of recognising who is a disease ‘candidate’, people acknowledge the changes they must enact to prevent illness.While candidacy offered these explanations of the type of person who could be a coronary candidate, the participants in Davison’s study acknowledged that the candidacy system was fallible. They identified exceptions to the rule about who ought to be a CHD candidate whereby individuals that met candidacy criteria never succumbed to disease, such as the ‘Uncle Norman’ figure who drinks and smokes all his life but lives to an old age—deemed by Davison et al. to be an ‘unwarranted survivor’  (p. 682)Recognising these candidacy violations also provides a mechanism to refute risk . AccountAdditionally, it is worth mentioning in this candidacy discussion, the occurrence and experience of serious illness, which has been recognised previously as ‘disruptive moments’  or ‘biogThe inference being that regardless of whether you participate in a healthy lifestyle or not, ‘when your time is up, your time is up’. This contributes to public distrust of the certitude of public health messages which infer that addressing modifiable risk factors will prevent disease, and subsequently the dismissal of these messages impacts on individual’s willingness to participate in health promoting behaviours, due the improbability that they would have any effect ,72. MoreThrough Davison’s study, candidacy emerged as a way to understand lay epidemiology ‘the routine observation of cases of illness and death in personal networks and the public arena’  (p. 678)How lay epidemiology frames an issue differs to traditional epidemiology. Lay epidemiology often contradicts expert views, and a broad literature has examined this lay-expert divide—where scientific knowledge is privileged over lay knowledge ,76,77. LBrown’s research  explorinOther research highlights these often-divergent views of lay epidemiology and science and the detriment of not including the lay views in the development of public health interventions ,78. One Cognisant of the above, Russel and Kelly  go furthIn the context of prevention efforts consideration of lay epidemiology is therefore crucial, and an exploration of candidacy brings another layer to this work. Women’s lay epidemiology regarding both alcohol and breast cancer may determine women’s assessment of their breast cancer candidacy. Their willingness to be involved in breast cancer prevention behaviours such as addressing modifiable lifestyle risk factors such as alcohol and screening may be contingent upon whether they see themselves as breast cancer candidates or not.Along with other research regarding CHD prevention ,85,86, iOne factor found to affect whether someone considers themselves or others to be a candidate is gender. A study by Emslie et al.  showed tGender also affects candidacy in relation to suicide prevention and can impact individuals’ willingness to accept or act on signs of distress. This is particularly apparent in young men, where signs of distress are typically dismissed as ‘alcohol talk’ , and forThe cultural familiarity of who presents as a ‘typical’ candidate also impacts willingness to assign candidacy to oneself or others. In suicide prevention studies often the deceased may be viewed as not the ‘suicidal type’  (p. 3) wResisted candidacy is another way candidacy may unfold. As described earlier, for women, coronary candidacy may be resisted based on gender, but candidacy can also be resisted based on the denial of risk factors. For example, individuals at-risk for diabetes within one study, acknowledged their risk factors, but denied these increased their candidacy and subsequently declined pre-diabetic screening, effectively resisting their candidacy . SimilarCompeting candidacy is also apparent, where other issues override the ability to accept or focus on candidacy. The need for individuals to prioritise other roles and responsibilities, prioritise other health issues or choose not to change associated lifestyle behaviours (should they accept their candidacy) , all proA large body of literature has explored lay understandings of cancer, however research specifically examining cancer candidacy is more limited. Two systematic qualitative reviews have captured the breadth of work regarding lay understandings of cancer. These reviews demonstrate how lay epidemiology shapes cancer related beliefs and behaviours, and the personal experience of cancer is instrumental in forming these views: ‘Witnessing the treatment, and sometimes death, of someone close was perceived as the most reliable source of information’   explored breast cancer candidacy. The first of these examined candidacy in relation to mammography screening through focus groups with women from diverse cultural backgrounds . CandidaThe second study exploring breast cancer candidacy conducted focus groups with women from African American neighbourhoods in the US . These wNumerous researchers have examined understandings of colorectal cancer and candidacy in the context of bowel cancer screening. Javanparast et al.  and WardThrough this review, key components of Davison’s original candidacy system have been presented  as shown in The key components of Davison’s candidacy system were seen across CHD, diabetes, suicide prevention and cancer. Within CHD and diabetes candidates were culturally familiar, conjuring images of the type of person that may be a candidate for illness, for example the ‘dead ringer’ for a diabetic candidate   or where social roles, identities or lifestyle issues take prominence over acknowledging one’s risk for illness (contested candidacy) or acting on addressing that risk. In some instances, however, individuals may have sufficient agency to address risk factors and enact lifestyle changes, and thus reverse candidacy. This is exemplified by the pre-diabetic individual who addresses lifestyle factors working to reduce or remove their diabetic risk and thus candidacy. In exploring suicide prevention, the literature suggests that widely held assumptions about what a suicidal person is like (depressed and withdrawn) can inadvertently result in overlooking those who are suicidal yet not presenting as such, termed ‘missed candidacy’. Lastly, candidacy can be understood as being collective, as illustrated in studies that have highlighted the social patterning of candidacy.Whilst some exploration of the candidacy concept has occurred in relation to cancer, it is perhaps timely to revisit the potential usefulness of this, particularly for those cancers that may have modifiable risk factors. As identified by Macdonald  (p. 577)The key purpose of this paper was to present the concept of candidacy, from its emergence within CHD to its application in other health contexts, and to elucidate the key components of the concept. In doing so, important questions regarding the applicability of the concept within primary cancer prevention have been posed. Re-purposing the interpretive framework of candidacy to further explore primary cancer prevention may be timely, given increasing scientific understandings of potentially modifiable cancer risk factors ,97. It iGiven that breast cancer remains the highest cause of cancer death in women, the clear epidemiological link between alcohol and breast cancer, and that mid-life women are high consumers of alcohol, it would seem a high proportion of women are indeed potential cancer candidates, notwithstanding other possible risk factors that may confirm their candidacy. As identified by Macdonald et al. , in cons


Background: The COVID-19 pandemic caused significant disruptions in cancer care, and preliminary research suggests that these disruptions are associated with increased levels of psychosocial distress among cancer survivors. The purpose of this study was to offer a descriptive report of the psychosocial functioning, perceived risk and fear of cancer progression, and COVID-19 pandemic impact and experiences in a unique, high-risk patient cohort: breast cancer survivors whose cancer treatment was delayed and/or changed due to the COVID-19 pandemic.Methods: This cross-sectional study included 50 women with dual carcinoma in situ, lobular carcinoma in situ, or invasive breast cancer whose cancer surgery was postponed due to the pandemic. As they awaited delayed surgery or shortly after they received delayed surgery, participants completed questionnaires on psychosocial functioning , their perceived risk and fear of cancer progression, patient-provider communication about disruptions in their care, personal impact of the pandemic, worry/threat about COVID-19, and COVID-19 symptoms/diagnoses. Descriptive statistics and bivariate correlations were computed among continuous study variables. Independent samples t-tests explored group differences in psychosocial functioning between survivors who were still awaiting delayed surgery and those who had recently received it.Results: Overall, the sample denied that the pandemic seriously negatively impacted their finances or resource access and reported low-to-moderate levels of psychosocial distress and fear about COVID-19. Twenty-six percent had clinically significant levels of fear of cancer progression, with levels comparable to other recent work. About a third were still awaiting delayed cancer surgery and this group reported lower satisfaction with communication from oncology providers but overall did not seem to report more psychosocial difficulties than those who already had surgery.Conclusion: Shortly before or after primary breast cancer surgery that was delayed due to the COVID-19 pandemic, this sample of survivors appears to be generally managing well psychosocially. However, many psychosocial difficulties  typically have an onset after the completion of treatment, therefore, research should continue to follow this cohort of cancer survivors as the pandemic’s direct impact on their care likely increases their risk for these difficulties later in survivorship. As of April 2021, there have been over 135 million COVID-19 cases and nearly 3 million deaths due to COVID-19 globally . IndividIn the United States, the COVID-19 pandemic caused significant disruptions in non-COVID-related health care including cancer care. On March 13, 2020, the American College of Surgeons recommended that elective surgical procedures – including most cancer surgeries  – be posDecisions to disrupt cancer care must carefully weigh the relative risks of COVID-19 exposure (and community spread) and poorer cancer prognosis due to care disruptions. Indeed, the impact of delays in breast cancer care on mortality has been well-documented . The resVery urgent, recommended the surgery in 2–4 weeks following the completion of chemotherapy; (2) Limited delay acceptable, recommended the surgery in 2–4 months (or longer if responding to neoadjuvant endocrine therapy); and (3) Lowest priority, recommended to wait until elective surgeries resume as usual. However, a recent review of the published guidelines for cancer care during the COVID-19 pandemic concluded that they are often discordant and based on limited evidence  level may also contribute to disruptions in cancer care during the COVID-19 pandemic. Breast cancer survivors who were diagnosed during the COVID-19 pandemic were found to refuse surgery at a higher rate than those diagnosed pre-pandemic, primarily due to fear of COVID-19 infection . It seemIn addition to uncontrollable system changes in their cancer care and their own psychological and behavioral responses to the pandemic, social distancing – strongly recommended for cancer survivors due to their high risk of COVID-19 infection – further increases this group’s risk of psychosocial difficulties. This includes loneliness and isolation , which aIn addition to the high rates of psychological distress in the general population during the COVID-19 pandemic , an emerMoreover, emerging evidence also suggests that disruptions in cancer care are related to these mental health symptoms ; breast The primary aim of the present paper was to provide a comprehensive baseline characterization of the psychosocial functioning of a cohort of breast cancer survivors whose cancer care had been delayed and/or changed due to the COVID-19 pandemic in the United States. While prior studies examined psychosocial functioning among breast cancer survivors across the continuum of cancer care, little is known about the cohort whose surgery was delayed and/or changed due to the pandemic. Therefore, we conducted a cross-sectional study of survivors who were awaiting delayed surgery or who recently underwent delayed surgery. Our goal was to provide a broad description of a sample from this unique cohort who may be at high risk of experiencing long-term psychosocial sequalae as a result of the COVID-19 pandemic.Impact of COVID-19 on Women Recently Diagnosed with Breast Cancer . The purpose of this study was to examine the psychosocial impact of the COVID-19 pandemic on women diagnosed with dual carcinoma in situ (DCIS), lobular carcinoma in situ (LCIS), or invasive breast cancer whose cancer surgery was postponed as a result of the pandemic. Following from their surgery status, all potential participants had non-metastatic (operable) breast cancer (Stage 0 to III). Eligibility for the study included women who (1) were diagnosed with DCIS, LCIS, or invasive breast cancer, (2) whose cancer surgery was postponed as a result of the COVID-19 pandemic, and (3) spoke English. Note that although LCIS is neither technically considered cancer nor “pre-cancer,” participants with DCIS/LCIS whose treatment plans included surgical intervention  to reduce the likelihood of disease progression and rule out any other disease process were included in this study. This decision was made based on the study’s focus on concerns about disease progression, the levels of which have been found in prior research to be comparable across patients DCIS/LCIS and those with stage I cancer , 18 denied postponements in their cancer surgery, four did not speak English, 23 actively declined, and 10 passively declined. Seventy-five agreed by phone to participate and 50 completed the informed consent and cross-sectional survey.Participant flow is detailed in As previously mentioned, breast surgeries at the Helen F. Graham Cancer Center and Research Institute resumed on May 15, 2020. Data collection for the present study occurred between June 27 and August 13, 2020. Therefore, by the time of data collection, most of the sample had received delayed breast cancer surgery (more detail described in section Results).n = 130) were mailed a letter containing an invitation to participate, a brief description of the study, and study contact information. Approximately 1 week after mailing this letter, all potential participants were contacted by phone by a clinical psychology doctoral student. During these phone calls, a script was used to invite potential participants to take part in the study, describe the study procedures, describe the potential risks and benefits of participation, and answer potential participants’ questions about the study. Five potential participants indicated that they did not have access to the internet and were therefore offered to complete the informed consent form and study questionnaire via mail. Those who could not be reached were contacted by phone up to two additional times  before being considered as passive decliners.A list of the pending breast surgery cases was maintained by the Helen F. Graham Cancer Center and Research Institute and was used to screen for potential eligibility. All cases with a diagnosis of DCIS, LCIS, or invasive breast cancer (n = 75) were sent additional information about the study, the informed consent form, and study questionnaire. Those who expressed interest in completing the informed consent and study questionnaire online were sent these materials via email (n = 70) and those who expressed interest in completing the informed consent and study questionnaire via mail (n = 5) were sent these materials via mail. Those who were sent study materials via email received up to three reminder emails and one reminder call (spaced approximately 4 days apart) if they had not yet completed the informed consent and study questionnaire online. Those who were sent the study materials via email were not provided with reminder emails or calls. Forty-nine participants completed the informed consent (signed and dated electronic form) and study questionnaire online and one participant completed the informed consent and study questionnaire via mail on paper, resulting in a total sample of 50 participants. Participants were not compensated for taking part in the study.Those who agreed by phone to participate  to five (“completely satisfied”).Two questions assessed survivors’ perceptions of communication with their health care team about delays and/or changes in cancer treatment due to the pandemic. The first question asked what, if anything, the oncologist told the survivor about how the delay/change in treatment might affect the risk of cancer progression; response options were, “It has been difficult for me to get the things I need due to the coronavirus (COVID-19)”] with an item assessing impact to health insurance coverage specifically [“My health insurance coverage has been negatively affected by the coronavirus (COVID-19)”]. Because psychosocial functioning had already been assessed in this study, two psychosocial impact items [“I have become depressed because of the coronavirus (COVID-19)” and “The coronavirus outbreak has impacted my psychological health negatively”] were replaced with one item assessing impact to household responsibilities [“My household responsibilities  have increased and/or are more difficult to manage due to the coronavirus (COVID-19)”]. Each item was rated from one  to seven (“very true of me”). Items were averaged to assess the overall COVID-19 impact, and the modified scale in this sample had acceptable reliability (α = 0.81).COVID-19 impact was assessed using a modified version of the Coronavirus Impacts Questionnaire-Short Version . The CorParticipants responded to the Personal Diagnoses/Symptoms Scale of the Coronavirus Experiences Questionnaire-Short Version , which iI am anxious or worried about surviving the coronavirus (COVID-19) if I caught it.” All four items were rated from one  to seven (“very true of me”). Responses were averaged to create a composite score, with greater scores reflecting greater COVID-19-specific threat sensitivity. The alpha coefficient (α = 0.91) reflected acceptable reliability.COVID-19-specific threat sensitivity was assessed using a modified version of the Perceived Coronavirus Threat Questionnaire-Short Version , which cHow concerned are you about your cancer progressing (growing or spreading in the same or another part of the body)?”] with responses ranging from one  to seven (“extremely concerned”). The second item assessed the perceived risk of progression (“Considering any delays or changes in your cancer treatment due to coronavirus (COVID-19), what do you think of your chance is of your cancer progressing?”), with a continuous slider scale ranging from 0% (“no chance of cancer progression”) to 100% (“cancer will definitely progress”). The final item assessed the perceived change  in progression risk due to COVID-19-related delays/changes in treatment .Three items measured the perceived risk of cancer progression. One item assessed overall concern about cancer progression , such that survivors who had already received their postponed breast cancer surgery were more satisfied with the communication from their oncology providers (M = 3.88) than those still awaiting their postponed surgery (M = 3.12). A moderate-sized effect (g = 0.56) was also observed for a mean difference in PROMIS Depression scores , indicating that survivors still awaiting surgery had somewhat higher levels of depressive symptoms (M = 52) than those post-surgery (M = 48). A moderate-sized effect (g = 0.53) was also found for the perceived risk of cancer progression  = 1.73, p = 0.091), such that survivors awaiting surgery also estimated that they had higher risks of cancer progression (M = 40%) than those post-surgery (M = 25%). The remaining t-tests revealed smaller mean differences (g < 0.4) between the groups .Independent samples This study aimed to provide a comprehensive description of the psychosocial functioning of a unique cohort, high-risk of breast cancer survivors in the United States whose cancer care had been delayed and/or changed due to the COVID-19 pandemic. These are survivors who had been recently diagnosed with non-metastatic, operable breast cancer and thus likely have a favorable prognosis, with a 5-year relative survival rate of 99% for localized breast cancer and 86% for regional breast cancer . Prior tAbout two-thirds of the sample had recently received their breast cancer surgery, which had been delayed due to the COVID-19 pandemic, whereas the remaining third comprised survivors still awaiting their delayed surgery date. The majority of these survivors had received neoadjuvant treatment (either endocrine therapy or chemotherapy), which may have been recommended to mitigate increased risks associated with delays in primary surgery. In terms of race and ethnicity, the sample is roughly comparable to the typical cancer survivor in this Mid-Atlantic region of the United States . HoweverDespite the fact that this sample was selected on the basis of their breast cancer treatment being delayed and/or changed due to the pandemic, 48% reported that their oncology providers never discussed with them how this disruption would affect their cancer prognosis . Most of the remaining participants (44%) reported that they were told the delay and/or change in their treatment would not change their prognosis or cancer progression risk. Because we do not have detailed medical records available for these participants, the accuracy of these statements cannot be estimated. Consistent with the relatively high proportion of participants who denied having a discussion with their providers about this, 18 survivors identified by the hospital records as having delayed and/or changed breast cancer surgery declined to participate because they did not believe that their cancer treatment was altered due to the pandemic. Unfortunately, we did not collect corroborating data from participants’ oncology providers to determine the extent to which these results reflect actual (objective) patient-provider interactions, participants’ comprehension of information communicated by providers, and/or other individual-level factors that may color their perception, memory, or judgment about prior discussions with their providers. Future research may be able to explore these questions by incorporating more detailed clinical data, data from patients’ oncology providers, and/or direct observation of patient-provider discussions about risk and prognosis. Nonetheless, it may have been challenging for oncology providers to navigate these discussions with survivors due to the unprecedented nature of the pandemic and limited empirical data on change in risk due to these delays. Importantly, despite this, most participants reported satisfaction with their communication by their oncology team. Although, interestingly and perhaps understandably, satisfaction was higher on average among those who already had surgery and lower among those still awaiting their delayed surgery.There also was a moderate-sized correlation between satisfaction with oncology provider communication and low COVID-19 impact. Sociodemographic factors may partially explain this effect; for example, individuals who are Black or living in poverty are both more likely to be affected by COVID-19  and lessWe also examined several indicators of psychosocial distress, including the perceived threat of COVID-19, FCP, generalized anxiety and depressive symptoms, sleep quality, and perceived quality of life. The pattern of findings for these variables suggested that overall, this sample on average reported low-to-moderate levels of psychosocial concerns and fear related to COVID-19. Given that extant data are suggestive of potentially poorer cancer prognosis when surgery is delayed (as was the case for these participants), we speculated that this sample is likely at higher risk for experiencing high FCP, which is already a relatively normative experience among cancer survivors even in the absence of a global pandemic. Results indicated that about a quarter of this sample experienced clinically elevated FCP, as defined by the most recent psychometric evidence . A recenPerhaps surprisingly, mean levels of FCP did not significantly differ between survivors pre‐ vs. post-surgery, yet prior work found delays in cancer care to be significantly related to survivors’ FCP . CriticaThis study had a number of strengths. Most notably, this was the first study, to our knowledge, to specifically target the assessment of breast cancer survivors whose primary cancer surgery was postponed due to the COVID-19 pandemic. Prior research on cancer survivors during the pandemic has not selected participants on the basis of their care being directly affected . At the same time, our sample was heterogeneous with regard to being pre‐ vs. post-surgery, receipt of neoadjuvant treatment, and sociodemographic variables. Nevertheless, there were also important limitations to this study. First, the sample size was relatively small and thus these results should be considered as tentative pending a well-powered replication. Second, while this sample was racially and ethnically representative of the patient population, participants were generally financially secure, highly educated, and did not report being severely impacted by the COVID-19 pandemic. Thus, more research is needed to better understand the needs of even higher risk groups of cancer survivors during and after the COVID-19 pandemic. Future research could benefit from identifying and describing other patient populations whose cancer care was affected by COVID-19, particularly patients who may be at higher risk, including those of lower socioeconomic status, as well as patients with other cancer diagnoses; this work may help to identify key sociodemographic or clinical characteristics that impact psychosocial response to COVID-19. Third and finally, the use of short forms or single items to assess psychological symptoms and multidimensional constructs may not fully capture these concepts, thus limiting the interpretation of the results.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Christiana Care Health System IRB approval: FWA00006557; CCC# 40079. The patients/participants provided their written informed consent to participate in this study.ES: conceptualization, data curation, formal analysis, methodology, and writing – original draft. CP: conceptualization, data curation, formal analysis, investigation, methodology, project administration, and writing – original draft. AO: conceptualization, methodology, and writing – review and editing. AF: writing – review and editing. SS: conceptualization, methodology, resources, and supervision, writing – review and editing. DD-W: conceptualization, resources, supervision, and writing – review and editing. LC: conceptualization, resources, supervision, writing – review and editing. J-PL: conceptualization, data curation, resources, supervision, and writing – review and editing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Hyperplasia of mammary gland (HMG) has become a common disorder in women. A family history of breast cancer and female reproductive factors may work together to increase the risk of HMG. However, this specific relationship has not been fully characterized.A total of 1881 newly diagnosed HMG cases and 1900 controls were recruited from 2012 to 2017. Demographic characteristics including female reproductive factors and a family history of breast cancer were collected. A multi-analytic strategy combining unconditional logistic regression, multifactor dimensionality reduction (MDR) and crossover approaches were applied to systematically identify the interaction effect of family history of breast cancer and reproductive factors on HMG susceptibility.In MDR analysis, high-order interactions among higher-level education, shorter breastfeeding duration and family history of breast cancer were identified . Similarly, in crossover analysis, HMG risk increased significantly for those with higher-level education (OR 36.39 [95% CI 11.47 to 115.45]), shorter duration of breastfeeding (OR 27.70 [95% CI 3.73 to 205.70]) and a family history of breast cancer.Higher-level education, shorter breastfeeding duration and a family history of breast cancer may synergistically increase the risk of HMG. Researchers have identified reproductive risk factors for HMG, such as late age at menopause, nulliparity and a lack of breastfeeding.3 Nevertheless, the aetiology of HMG remains largely unknown. A family history of breast cancer is an important indicator for women's risk of developing breast cancer.4 Recently there has been growing recognition that large sample sizes are needed in order to identify heredity variants that have effects modified by the environment as well.5 Heredity–environment interactions have the potential to illustrate the biologic causes of disease, distinguish individuals for whom risk factors are most related and develop precision medicine.6 However, few researchers have explored the interaction between a family history of breast cancer and HMG. Furthermore, existing studies include only a single statistical method to study the interaction between a family history of breast cancer and HMG, lacking the internal validation and decreased statistical power to identify underlying heredity–environment interactions.7Hyperplasia of mammary gland (HMG), a multifactorial complicated disease, accounts for >70% of all breast diseases that occur among middle-aged women and is highly associated with breast cancer.Using data collected in a large community-based case–control study, we assessed the correlation of HMG with a family history of breast cancer and reproductive factors in women self-reporting first- and second-degree relatives. We adopted multi-analytic strategy to scientifically examine the interactions between hereditary and female reproductive factors. Several statistical approaches, including traditional multiple logistic regression, multifactor dimensionality reduction (MDR) and crossover analysis were applied to explore the relationship between high-order hereditary and reproductive factors for HMG susceptibility.This study is based on the National Basic Public Health Service Project, which is provided free of charge for both urban and rural residents by the Chinese government. A total of 1966 patients who were newly diagnosed as HMG by colour Doppler ultrasonography from October 2012 to December 2017 were collected. Meanwhile, 1993 HMG-free controls were chosen from the community health service centre of Harbin. Inclusion criteria were female subjects newly diagnosed with HMG, age >35 y, living in Harbin for at least 6 months and who agreed to a colour Doppler ultrasound examination. Patients with mastitis, angiosarcoma, tumour of the mammary glands, breast cancer or other cancers were excluded. A total of 85 cases (4.4%) and 93 controls (4.7%) were excluded because of missing information and a total of 1881 cases and 1900 controls were enrolled  and female reproductive factors  were obtained using a structured questionnaire administered by trained interviewers face to face. In this research, history of breast cancer was defined as breast malignancy in a first- or second-degree relative . Regular menstruation was considered as a menstrual time of 2–7 d and a menstrual cycle of 24–35 d. Menopause referred to the specific period from the appearance of endocrine, biological and clinical characteristics related to menopause to the postmenopausal period. Fibrous (cystic) and single-type HMG was included in our study. According to the fifth edition of the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) guidelines, cases classified as stages II–V were included in our study . The lesion site  was taken used as the BI-RADS grade.8 The potential confounding variables were controlled in the process of analysing the interactions. P-values <0.05 were considered statistically significant and all p-values were two-tailed. All statistical analyses were performed with SPSS Statistics , SAS  and MDR software .The odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were summarized to estimate the associations between reproductive factors and HMG risk by univariate and manual stepwise multivariate logistic regression. All references values for exposure were the lower level of the variables. Interactions between a family history of breast cancer and female reproductive factors were evaluated by MDR. The MDR approach includes a cross-validation procedure that minimizes the possibility of false-positive results by dividing the data into a testing set and a training set. Cross-validation consistency (CVC) provided a summary for the number of cross-validation intervals for discovering a particular model. Higher numbers mean more stable results. The joint effects between female reproductive factors and a family history of breast cancer on the risk of HMG were analysed by the crossover method. Additive interactions were calculated by the relative excess risk of interaction (RERI), attributable proportions of interaction (API) and the synergy index (SI) as described by Andersson et al.The female reproductive factors and ORs for HMG are presented in Table Married women, manual workers, regular menstruation, breastfeeding history, longer breastfeeding duration, early age at menopause and early parturition had 0.26, 0.27, 0.27, 0.25, 0.27, 0.71 and 0.02-fold reductions in the risk of HMG when compared with controls. A significant increase in HMG risk was associated with later age at first delivery and family history of breast cancer , whereas occupation type and breastfeeding duration were statistically negatively associated with HMG , breastfeeding duration , the interaction effect of education level and breastfeeding duration  and family history of breast cancer on HMG , attributable proportions of interaction (API) and synergy index (SI) are indicators for additive interactions. There were no statistically significant additive interactions between education level, breastfeeding duration and family history of breast cancer on the risk of HMG Table .9 mental factors10 and genetic factors11 have been confirmed to impact HMG. Treatments for HMG include hormone replacement drugs,12 traditional Chinese medicine13 and lifestyle interventions.14 However, the pathogenesis of HMG is still unclear.HMG, characterized by breast pain and lumps, is a common disease in women. Endocrine disorders,15 Additionally, an antagonistic effect between a family history of breast cancer and breastfeeding duration was also observed, which was consistent with the published literature.16 Based on these results, women with a family history of breast cancer may reduce their risk of HMG through adjustments in reproductive choices.17 Third, since the additive model might be better to explain the biologic interaction, we also estimated the RERI, API and SI by additive models, but we did not find a statistical difference. Sample sizes may have led to a reduction in statistical power.18 Although we did not find an effect of education level or breastfeeding duration combined with a family history of breast cancer, several lines of evidence suggest that our findings are biologically plausible. Our research also found that HMG individuals with a family history of breast cancer had a greater chance of developing neoplasia.19 The activation of Akt-1, which peaks in lactation, regulates survival of epithelial cells. A shorter breastfeeding duration decreased Akt-1 significantly, which my contribute to HMG.20 A higher education level is often accompanied by high stress, which is thought to be connected with an increased risk of breast disease.21 Normal growth of the mammary gland involves endocrine signals from the hypothalamic–pituitary–gonadal axis.22 Stress has been shown to disrupt the function of the endocrine system and increase susceptibility to HMG.23 Additionally, an increasing level of inflammatory burden and hypothalamic–pituitary–adrenocortical axis dysregulation subsequent to stress may also cause HMG.24 These observations indicate that heredity–environment interactions might be especially important for HMG.25 Therefore HMG prevention strategies should be individualized according to an individual's exposure to risk factor profiles.In this case–control study of HMG, evidence was found that the risk of HMG is influenced not only by a family history of breast cancer but also by breastfeeding duration and education level. Possible interactions between hereditary and reproductive factors of HMG were noted. Various algorithms were used to explore the interactions between a family history of breast cancer and female reproductive factors. First, MDR was used to analyse the interactions of six environmental factors that were statistically significant in multiple logistic regression. High-dimensional interactions, including education level, breastfeeding duration and family history of breast cancer, were detected. Second, we adopted a crossover analysis method and found a strong synergistic effect between a family history of breast cancer and higher education level after adjusting for BMI and occupation. Therefore more attention should be paid to enhancing awareness and health education among HMG women with higher education levels and a family history of breast cancer.26 MDR can identify putative high-order interactions, but is limited in analysing main effects in many diseases.27 Crossover analysis can evaluate the independent and joint roles of genetics and exposure on disease hazard.28 However, it can only analyse the interactions between binary variables.29 Recent studies have shown that multiple complementary analytical strategies, including logistic regression and MDR, could improve statistical power to identify underlying heredity–environment interactions.31Heredity–environment interactions are consistently distinguished by both non-parametric and parametric statistical models. Logistic regression has the advantage of analysing for the main effect. When high-order interactions involving multidimensional elements are taken into account, they may be limited in dealing with simultaneous factors.Results from MDR and crossover analysis consistently show that a family history of breast cancer is the most significant single risk for HMG and HMG risk is substantially associated with education level and breastfeeding duration interactions. In this research, the MDR and crossover analysis validated each other and emphasized the repeatability of our results.Nevertheless, this study still has some limitations. The association between a family history of breast cancer and female reproductive factors was analysed. Further studies are imperative to understand whether the interactions are related to other factors such as dietary habits, lifestyle and hormone replacement therapy. Furthermore, the results obtained in this research could be affected by recall bias, which frequently appears in case–control studies, thus replication in other independent samples of observed interactions is needed to verify our results. Moreover, the number of cases in the strata was relatively small. Therefore these variables may not be adequately powered to assess interactions. Expanding the sample size or finding other more applicable statistical analysis methods to analyse interactions is needed in future studies. Lastly, all patients with HMG did not have a tissue biopsy, so the related mechanism of patients with different types of HMG is needed in further research.High-order interactions of higher-level education, shorter breastfeeding duration and a family history of breast cancer might synergistically increased HMG risk.


MTHFR C677T and A1298C), methionine synthetase (MTR A2756G), and methionine synthase reductase (MTRR A66G) polymorphisms involved in folate pathway and breast cancer risk, and the interaction between these polymorphisms and tobacco and alcohol consumption. Furthermore, we evaluated the association between these polymorphisms and clinicopathological variables. This case-control study included 606 Brazilian women, comprising 128 patients with breast cancer and 478 controls. MTHFR and MTR polymorphisms were genotyped using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and MTRR polymorphisms using real-time PCR. Age ≥50 years  and alcohol consumption  were associated with an increased risk of breast cancer. For MTHFR A1298C, we observed a reduced risk of developing breast cancer in the codominant model , recessive model , and log-additive model . Women aged ≥50 years and those who are alcohol consumers had increased susceptibility to breast cancer, and MTHFR A1298C modulated the risk for this disease. This is the first study to evaluate the association between polymorphisms in folate metabolism and breast cancer in the northwest region of São Paulo State, Brazil.We investigated the association between methylenetetrahydrofolate reductase ( Breast cancer is the most common cancer and the second most common cause of mortality . Thus, tThere are potential biomarkers, such as polymorphisms of the folate pathway or one-carbon pathway. The latter pathway is responsible for the donation of the methyl group, which is further used in important processes such as DNA methylation, histone methylation, and protein methylation . Folate MTHFR C677T polymorphism has been associated with susceptibility to developing breast cancer among Asian women, but no association was observed in Caucasians  on cancer susceptibility. The ucasians , suggestucasians , indicatMTHFR C677T (rs1801133), MTHFR A1298C (rs1801131), MTR A2756G (rs1805087), and MTRR A66G (rs1801394) polymorphisms and the risk of developing breast cancer in women from Northwest São Paulo State, Brazil. We further examined the association of risk factors, including age, smoking habits, alcohol consumption, number of pregnancies, body mass index, hormone therapy, and clinicopathological variables, with breast cancer development. Overall, we showed that the MTHFR A1298C polymorphism reduced the risk of developing breast cancer, whereas alcohol intake increased the risk.Here, we aimed to investigate the association between The present case-control study was approved by the National Ethics Committee (CAAE - 04069612.1.0000.5415), Sao Jose do Rio Preto Medical School (FAMERP), Sao Paulo, Brazil (process number: 84397). All individuals agreed with the informed consent form, according to Resolution 466/12 of the National Health Council (CONEP). This study is consistent with STrengthening the Reporting of Observational Studies in Epidemiology (STROBE) .We evaluated 606 eligible women, comprising 128 patients (case group) after total excision or biopsy were diagnosed and histologically confirmed to have breast cancer and 478 healthy women volunteers (control group); only women without any history of cancer or familial history of cancer aged >40 years were included. All women were admitted between January 2013 and January 2015 to the Hospital de Base, São Jose do Rio Preto Medical School, São José do Rio Preto, Brazil. The control group was recruited from blood donors who met the government guidelines for blood donor selection (http://www.hemonline.com.br/portarias/rdc153/indexframe.htm). During recruitment of the control group, efforts were made to maintain a similarity in age and geographic region between the control and case groups. In individuals who were most recently recruited to the control group (n=219), we also performed a standardized questionnaire to obtain personal data, demographic and epidemiological factors such as lifestyle , reproductive history , and family history of cancer (breast cancer and/or other cancers). Measurements of height and weight were used to calculate the body mass index (BMI). Additional information about the diagnosis, histological type, tumor location, tumor size (T), regional lymph node involvement (N), presence of metastasis (M), progesterone receptor (PR), estrogen receptor (ER), and human epidermal growth factor receptor-type 2 (HER2) overexpression were obtained from the medical records and pathology reports. TNM classification of malignant tumors was classified according to the UICC/AJCC of 2010  and grouWe considered smokers who consumed at least 100 cigarettes during their lifetime and alcohol drinkers as those who consumed more than one drink weekly  .MTHFR C677T (rs1801133), MTHFR A1298C (rs1801131), and MTR A2756G (rs1805087) polymorphisms were determined using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) with the following primers: MTHFR C677T, sense 5′-TGA AGG AGA AGG TGT CTG CGG GA-3′, anti-sense 5′-AGG ACG GTG CGG TGA GAG TG-3′; and MTR A2756G, sense 5′-CCAGGG TGC CAG GTA TAC AG-3′, anti-sense 5′-GCC TTT TAC ACT CCT CAA AAC-3′. Genotyping of the MTHFR C677T polymorphism was accomplished using the restriction enzyme, HinfI. The resulting fragments were 198 bp , 175 bp, and 23 bp . The MTR A2756G polymorphism was genotyped using the restriction enzyme, HaeIII. The resulting fragments were 413 and 85 bp  and 290, 123, and 85 bp  (MTRR A66G (rs1801394) polymorphisms was performed using the Real Time PCR-SNP Genotyping Assay  - Assay ID: C_3068176_10 (20X), in following the manufacturer’s instructions. The reactions were performed using the StepOnePlus™ Real-Time PCR System  . GenotypR System . To imprMTHFR C677T (reference: genotype CC), MTHFR A1298C (reference: genotype AA), MTR A2756G (reference: genotype AA), and MTRR A66G (reference: genotype AA). The clinicohistopathological characteristics were T classification (reference: T1 and T2), N classification (reference: no lymph nodes affected), and M classification (reference: no metastasis). Logistic regression was adjusted for age, sex, smoking habits, and alcohol consumption. Multiple logistic regression was used to determine the effects of variables on breast cancer risk and clinicopathological characteristics using the Minitab/Windows program - Version 14.0. The model included the following variables: age , smoking habits (reference: non-smoker), alcohol consumption , number of pregnancies , hormone therapy (reference: not using hormone therapy), The Hardy-Weinberg equilibrium (HWE) was assessed using the chi-square test using the program SNPStats (available at http://bioinfo.iconcologia.net/SNPstats_web). The SNPstats online computer program was used to analyze the following models: 1) codominant (heterozygous vs. homozygous wild type and polymorphic homozygous vs. homozygous wild type), 2) dominant (heterozygous plus polymorphic homozygous vs. homozygous wild type), 3) recessive (polymorphic homozygous vs. homozygous wild type plus heterozygous), 4) overdominant (wild homozygous vs. heterozygous plus polymorphic homozygous), and 5) additive (weight polymorphic homozygote vs. heterozygote 2 plus homozygous wild-type) . The relp<0.001) and who consumed alcohol  had an increased risk of breast cancer. Smoking habits, number of pregnancies, BMI ≥25 kg/m2, and hormone therapy showed no association with the risk of breast cancer (data not shown). Regarding clinicohistopathological characteristics, we found that 94.6% of all breast cancers were invasive ductal carcinomas, 4.6% had lobular carcinoma, and 0.8% mixed ductal and lobular carcinoma. Regarding TNM classification, 62.5% of cases were T1 or T2, 42.2% of cases had affected lymph nodes, and 34.4% of cases had metastasis. The frequency of breast cancer subtypes was as follows: 48.4% for luminal B, 26.6% for luminal A, 12.5% for triple-negative, and 10.1% for HER2 overexpression.In this study, we observed that women aged ≥50 years old , MTHFR A1298C (rs1801131), MTR A2756G (rs1805087), and MTRR A66G (rs1801394) polymorphisms; tumor size; lymph node involvement; and metastasis are shown in MTHFR A1298C (rs1801131) polymorphism reduced the risk of developing breast cancer in the codominant model , recessive model , and log-additive model . No significant association was found between the MTR A2756G (rs1805087), MTRR A66G (rs1801394), and MTHFR C677T (rs1801133) polymorphisms analyzed and models (p>0.05). The genotype distributions of MTHFR C677T (rs1801133), MTHFR A1298C (rs1801131), MTR A2756G (rs1805087) and MTRR A66G (rs1801394) polymorphisms and breast cancer risk are presented in The analyses of MTHFR C677T (rs1801133)  and MTR A2756G (rs1805087)  polymorphisms. In the control group, MTHFR A1298C (rs1801131) and MTRR A66G (rs1801394) polymorphisms showed disequilibrium (p=0.01 and p=0.03). The frequencies of variant alleles for SNPs (MTHFR C677T (rs1801133) polymorphism; allele A, 80% case and 72% control for the MTHFR A1298C (rs1801131) polymorphism; allele A, 82% case and 79% control for the MTR A2756G (rs1805087) polymorphism; and allele A, 60% case and 57% control for the MTRR A66G (rs1801394) polymorphism.HWE analysis  showed tfor SNPs  were: alp<0.001) and alcohol consumption (p=0.021) were risk factors for breast cancer in women. Women aged ≥50 years have been associated with an increased risk of breast cancer in other studies (p<0.001) in women in Malmo  and MTRR A66G (rs1801394) polymorphisms. HWE represents the non-evolution and homogeneity of a population. However, the diversity of this population, models, and complexity of the diseases can lead to an imbalance between sample groups; therefore, a control group is more likely to be in disequilibrium  reduced breast cancer risk in women with the 1298CC genotype compared to that of the AA genotype in the Scotland population  did not observe an association with breast cancer subtype. Tumors negative for hormone receptors and positive for HER2 have a worse prognosis in breast cancer (MTHFR C677T, MTHFR A1298C, MTR A2756G, and MTRR A66G polymorphisms in folate metabolism. Regarding breast cancer subtype, our study observed no association between the polymorphisms studied and luminal A, luminal B, HER2 overexpression, and triple-negative subtypes. is study  found noMTHFR C677T, MTR A2756G, and MTRR A66G polymorphisms and breast cancer development. However, women with the 1298CC polymorphic genotype of the MTHFR gene had a lower risk of breast cancer development. Therefore, further studies are warranted to confirm the role of these SNPs in breast cancer development.Our study is the first to show the profile of the female population from Northwestern São Paulo State, Brazil. Since few studies have been performed analyzing folate metabolism polymorphisms and clinical and histopathological features of breast cancer. Overall, this study states that age ≥50 years and alcohol consumption increases the risk of breast cancer in women. We did not find a significant association between