The goal of this study is to establish the differences in Type D personality and Big five personality traits between a group of newly diagnosed breast cancer patients and a group of controls.A comparative study of breast cancer patients and women without previous history of cancer was carried out. We used Type D Scale-14 as an instrument for the assessment of the type-D personality pattern and NEO-FFI for the assessment of the Big Five personality traits. Conditional logistic regression models were used to estimate odds ratios and 95% confidence intervals were applied for breast cancer by personality trait factors.Negative affectivity (NA) (OR = 4.45 95% CI: 1.96–10.61), neuroticism HIGH , openness to experience HIGH (OR = 3.47 95% CI: 1.11–11.49), were associated factors significantly related to an increased risk of breast cancer, whereas Social Inhibition (SI) was associated factor with a decreased risk of breast cancer (OR = 0.40 95% CI: 0.16–0.92).This was the first case-control study which analyzed NA and SI traits in breast cancer patients. SI as a breast-cancer risk decreasing factor might indicate that expressing negative emotions is not always a healthy mechanism of their regulation. The relationship between cancer and personality causes strong controversy, indirectly leading to posing the question of whether cancer is a psychosomatic disease. The research of Greer and Morris on a group of women with breast cancer defined the behavior pattern later named Type C behavior , 2, alsoResearch exists which supports personality as a risk factor for cancer –9. Howevth, 2021). What links these two cases is that for several years both of these patients had been undergoing pharmacological treatment for depressive disorders and were referred to a psychotherapist by the author. Psychotherapy was suggested due to the patients' poor anger management skills which led them to deny and block it so much so they were consciously not experiencing it when their borders had been breached. During one of the author's conversations with their therapist on the patient's condition she spontaneously remarked that should they go on like this, they would soon get cancer. The statement was made 2 years prior to the patient's diagnosis; the question which arises then is why something which in interpersonal contact the therapist found obvious is not detected on the level of prospective studies.The senior author's clinical experience of over 20 years of working with patients with emotional disorders indicates that on a level of particular patients such a connection might indeed exist. There have been two clinical cases which have especially stuck with the author, i.e., two patients both of whom were diagnosed with cancer in 2020–a 58-year-old woman who got breast cancer and a 44-year-old man who got melanoma malignum  without taking stock of non-specific, non-homogeneous or incommensurable factors such as each patient's unique life story, chance events and resultant stress, coping methods or the impact of social support. It would indeed be very difficult to include patient's unique life stories and their social relations in prospective studies; therefore, the authors cannot stress enough the importance of conducting case-control studies because results thus obtained might help improve designing prospective studies.The distressed personality  concept was developed in the 1990's by Johan Denolett. Denolett suggested it may be a significant risk factor in cardiovascular diseases, in particular hypertension and ischemic heart disease . Type D There are few studies of Type D personality in the field of oncology. The existing studies mainly focus on colorectal cancer survivors (CRC) among whom nineteen percent can be classified as having a Type D personality . These pThe goal of this study is to establish the differences in Type D personality and the Big Five Personality traits between a group of newly diagnosed breast cancer patients and a group of controls.A comparative study of breast cancer patients and women without previous history of cancer was carried out. Inclusion criteria entailed age between 18 and 80, diagnosis of breast cancer (C50 according of ICD-10), no history of any serious life-threatening diseases or any serious psychiatric disorders .Subjects in the breast cancer group were consecutively referred to the Breast Unit at the Lower Silesian Oncology Centre in Wroclaw, for active surgical treatment carried out between December 2015 and January 2017. The average time from diagnosis of breast cancer to admission to the Breast Unit for surgery was 2 months.Women in the “comparison group” were recruited from the general population.Prior to data collection, study approval was obtained from the local ethics committee (KB- 619/2015) and performed in accordance with the Helsinki ethical standard.Medical and sociodemographic data were obtained from the participants and from their medical files, and recorded using a self-evaluation questionnaire. In order to measure personality traits and depressive symptoms the following measuring instruments were applied:The NEO Personality Inventory (NEO-FFI)–a self-administered questionnaire, consisting of 60 statements that are rated on a 5-point Likert scale, ranging from 1- “completely disagree” to 5- “completely agree” (5). The inventory is based on the theory of the Big Five personality traits and allows to assess personality dimensions such as openness to experience, conscientiousness, extraversion, agreeableness, neuroticism . The scoType D Scale-14 is a well-established and widely used instrument for the assessment of the type-D personality pattern . The scaA patient conducts a self-assessment of their own behavior using a 5-item scale in which 0 means a false statement, and 4 a true statement. The NA and SI scales can be scored as continuous variables (range 0–28) to assess these personality traits independently. A person manifests a type-D personality if his/her result on both scales (SI and NA) equals or exceeds 10.The measurement has been translated and validated to use in the Polish population with satisfactory internal consistency .U test for quantitative variables. Multivariate logistic regression was used to assess independent effect personality traits and how they correlate with belonging to the breast cancer group. To exclude confounding effects, all models were adjusted for age. The no cancer control group was used as the reference group and 95% CI were calculated for all odds ratios.The Shapiro-Wilk test and visual assessment of distribution were used to analyze the normality of the data. Comparisons between the female breast cancer and no cancer control groups were carried out using the Chi-squared test or Fisher's exact test for categorical variables, and Mann–Whitney p < 0.05. Analyses were performed using statistical software package R for Windows .Significance level was set to Among the 200 women with breast cancer contacted, 105 accepted to participate (52.5%). The stated reasons for refusal included “lack of interest,” “being too tired,” and “not wishing to speak about illness.” Among 105 women who accepted to participate, 99 fulfilled the inclusion criteria. The 6 non-eligible women were excluded due to their diagnosis being Benign neoplasm of breast (ICD-10 D24).Of the 100 participants in the comparison group recruited in the study, only women with no missing data for all the variables considered in the analysis were included.p = 0.000), but not marital status (p = 0.303), education level (p = 0.126) or having children (p = 0.278) . The cancer and comparison groups were found to differ with regard to age (= 0.278) .Analysis of personality traits measured in NEO-FFI revealed that in the breast cancer group the percentage of participants with Neuroticism HIGH (25.25 %) is statistically significantly larger than in the control group (10.12%) .p = 0.11).Among breast cancer patients, 40 (40.40%) had type D personality as compared to 22 (27.84%) of the controls. However, the difference between the groups was not statistically significant (p = 0.000). 51 % of Patients Had Social Inhibition in Comparison to 46 % of Controls (NS). The Mean Score in Negative Affectivity Scales Was Significantly Higher in the Breast Cancer Group-12.65 (6.19 SD) Compared to 8.12 (5.06 SD) in Controls (p = 0.000).67 % Breast Cancer Patients Exhibited Negative Affectivity in Comparison to 35 % in Control Group  and the group of controls (9.02 ± 6.38).Multivariate logistic regression revealed the following . ParticiThe main finding of our study is that Negative Affectivity is associated factor (OR 4.45) to the risk of the breast cancer group, while SI lowers the risk (OR 0.40). The precedence of type D personality, referring to the combined traits of NA and SI, does not differ significantly between the groups.r = −0.61) and NA strong positive associations with neuroticism (r = +0.74). In our investigation neuroticism was second to NA among factors increasing the risk of belonging to the breast cancer group (OR 3.97). Extraversion, in turn, was not such a factor. Denollet suggested that the SI trait is not identical to the introversion trait. The SI trait focuses on the interpersonal dimension of introversion (low self-expression) rather than the intrapsychic  dimension (The obtained results indicate that the tendency to experience increased negative emotions (NA) was linked to belonging to the breast cancer group, whereas inhibition of emotional and behavioral expression (SI) decreased this risk. The outcome surprised scholars because cancer-personality is associated with neuroticism and introversion , 2. De Fimension . The resimension .The findings of our study according to which the tendency to experience increased negative emotions (NA) is associated factor to the risk for belonging to the breast cancer group, whereas inhibition of emotional and behavioral expression (SI) lowers that risk contribute to the discussion on emotion regulation mechanisms and their role in cancer. Regulating emotional distress by active rather than evasive strategies is considered more adaptive because it relates to lower depression . Women wThe authors believe that the results they obtained point toward aspects related to the significance of emotion expression as a healthy mechanism of its regulation, i.e., a factor which decide about the ambiguity of studies on the association between personality and cancer. First, the author's psychotherapeutic experience indicated that expressing emotions is not always identical with their conscious experiencing–and it is the high awareness of experienced emotions, the ability to identify and name them which facilitates the organism's auto-regulation. Second, awareness of experienced emotions at the moment of a particular emotion's occurrence in the organism plays an important role in fostering a healthy auto-regulation because it enables expressing emotions for the purpose of neutralizing the factor which engendered them, e.g., expressing anger in order to protect our boundaries when they are being breached. As the authors see it, expressing anger post factum–without being aware of its causes–might only lead to estranging others and result in loneliness and lack of social support, especially when the person does not understand the reasons of the situation and hence does not take responsibility for it. Therefore, paradoxically in those cases when a person is not aware of their negative emotions –and is not able to relate them to factors which might be causing them–SI might perform a protective function not allowing for their external expression which could only deepen a feeling of loneliness. All these aspects are easily readable in the patient-therapist relationship but prove difficult or even impossible to capture in prospective studies.The second finding of our study is that for participants with high neuroticism (7-10 stens–Neurotism HIGH) the odds ratio for entering the breast cancer group was 3.97. These results correspond with research indicating that neuroticism and negative affect are related to cancer onset and mortality , 9, 32.Higher neuroticism is associated with difficulty in regulating negative emotions .Those high on the neuroticism scale manifest heightened degrees of anxiety, vulnerability, sadness, angry hostility, irritability, impulsiveness, fearfulness, helplessness, self-consciousness, embarrassment, and the inability to cope with stress.High neuroticism is associated primarily with negative health behaviors , 35. OtoNeuroticism might affect the immune system by changing the quality and quantity of stressors, leading not only to a greater presence of stressors  but also influencing the response to stressors . In moreThe third finding of our study posits that individuals with high results on the openness to experience scale had a higher associated factor to the risk of entering the breast cancer group. Openness reflects an open attitude toward experiences, beliefs and people. Individuals with high results on the openness scale are curious and unconventional. Nolan et al. found openness to experience as a significant predictor of breast lump check and mammogram while Bahat found that High extraversion, high openness and high conscientiousness predicted a greater participation in breast self-examination , 41. StuBoeft et al. revealed an association between increased openness to experience and an increased number of medical services and Toivonen et al. , 44 founOur finding that individuals with high results on the openness to experience scale had a higher associated factor to the risk of entering the breast cancer group according to the authors, could be connected with the treatment Centre in which our study was carried out.Breast cancer is the most common cancer among Polish women. Each year over 18 000 women hear such a diagnosis and one in three cases end in the patient's death because the breast cancer is still very frequently diagnosed in an advanced stage. The reason for this is that women are afraid of oncological treatment, incl. due to the many negative changes in body image during treatment . The LowThe study was carried out on a small population sample. Personality traits were assessed for 52% of patients who reported for treatment and gave their consent for participating in the study, which might mean it is unrepresentative. Since different geographic populations have different dispositions, this study surveyed populations only from Poland. Given the cross-sectional design of the investigation, prospective studies are needed to understand the causal nature of our results.Neuroticism and negative affectivity were the strongest associated factors to the risk of the breast cancer group in comparison to the control group. Patients with such a personality profile in groups of increased breast cancer risk should be especially targeted for psychological and social support.This was the first case-control study which analyzed the NA and SI traits in breast cancer patients. NA was significantly associated factor related to an increased risk for belonging to breast cancer, whereas, SI was associated with a decreased risk of breast cancer. Inhibition of emotional expression (SI) as an associated factor of decreased risk of breast cancer might indicate that expressing negative emotions is not always a healthy mechanism of their regulation. The authors believe that these findings might contribute to a better understanding of the association between personality traits and breast cancer.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 Wroclaw Medical Univeristy Ethics Committee. The patients/participants provided their written informed consent to participate in this study.TP, IW, and RM contributed to conception, design of the study, and wrote sections of the manuscript. IW organized the database and performed the statistical analysis. IW and TP wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.This study was supported by Wroclaw University Grant STM.C230.16.024 and SUB.C230.21.013.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Examine the effect of tamoxifen and aromatase inhibitors (AIs) on the risk of 12 clinically relevant cardiovascular outcomes in postmenopausal female breast cancer survivors.We carried out two prospective cohort studies among postmenopausal women with breast cancer in UK primary care and hospital data (2002–2016) and US Surveillance, Epidemiology and End Results-Medicare data (2008–2013). Using Cox adjusted proportional hazards models, we compared cardiovascular risks between AI and tamoxifen users; and in the USA, between users of both drug classes and women receiving no endocrine therapy.10 005 (UK) and 22 027 (USA) women with postmenopausal breast cancer were included. In both countries, there were higher coronary artery disease risks in AI compared with tamoxifen users . However, comparisons with those receiving no endocrine therapy (US data) showed no higher risk for either drug class and a lower risk in tamoxifen users . Similar patterns were seen for other cardiovascular outcomes . As expected, there was more venous thromboembolism in tamoxifen compared with both AI users and those unexposed.Higher risks of several cardiovascular outcomes among AI compared with tamoxifen users appeared to be driven by protective effects of tamoxifen, rather than cardiotoxic effects of AIs. Oestrogen or progesterone receptor positive breast cancer (ER/PR+) accounts for 83% of all breast cancer, and endocrine therapies are recommended to minimise risk of recurrence.Tamoxifen inhibits the growth of breast tumours through competitive antagonism of oestrogen at its receptor. It is known that oestrogen agnostic effects of tamoxifen lowers total serum cholesterol by 10%–20% and low-density lipoprotein levels by 15%–22%, which could explain any protective effects of tamoxifen on the cardiovascular system.Using data on women with postmenopausal breast cancer from two large electronic medical record datasets, we therefore aimed to: (i) examine the consistency of estimated associations between endocrine therapy drug class (AI/tamoxifen) and CVD risk between UK and US cohorts and (2) compare CVD risks between users of both drug classes and ER/PR+ breast cancer patients unexposed to endocrine therapy to investigate the key question of whether differences between the two drug classes were driven by underlying harmful or protective associations.We assembled two separate nationally representative cohorts of women with incident postmenopausal breast cancer using prospectively collected data from the UK and USA. In the UK, we used Clinical Practice Research Datalink primary care data (CPRD) and linked Hospital Episode Statistics (HES).10.1136/heartjnl-2020-317510.supp1Supplementary dataDifferences in available data within the two cohorts are explained in We identified women with CPRD and HES data over 54 years  for 12 months prior to the month of cancer diagnosis from 1 January 2008 (Medicare Part D data are available from 2007) and 31 December 2013 (last capture of cancer cases in SEER). Women with an endocrine therapy prescription prior to their breast cancer diagnosis were excluded. Follow-up began 1 year after the date of breast cancer (hereafter index date). Women were excluded if prior to their index date they: died, discontinued from Medicare Parts A, B or D, had any other cancer diagnosis or were diagnosed with the CVD event of interest (within a 3-year look back period to ensure likelihood of capturing a prior event was not dependent on age as Medicare follow-up starts at 65 years).https://doi.org/10.17037/DATA.177), and National Drug Codes and Healthcare Common Procedural Coding System (HCPCS) procedure codes in the USA (https://doi.org/10.17037/DATA.177) and ICD-9 and HCPCS codes in the US study ; in the US study, we were additionally able to adjust for race, region and use of anticancer therapies . A full comparison of the covariates considered in the UK and USA is in Observation began at index date and ended at the earliest of: a CVD event of interest; diagnosis of another (non-breast) cancer; death; and transfer out of CPRD/end of Medicare Parts A, B or D enrolment. Observation could also end at the end of the study period, which was 31 March 2016 in UK and 31 December 2014 in USA.Separate analyses were conducted in UK and USA and for each CVD outcome. Distributions of characteristics at index date were described. Number of events and crude incident rates of each outcome of interest by exposure were calculated. Cox proportional hazards models with an age timescale were fitted for each outcome of interest to obtain unadjusted HRs and 95% CIs for the association between endocrine therapy use and outcome; those with a diagnosis of the specific outcome of interest before the index date were excluded from the analysis for that outcome. All covariates were then added to obtain fully adjusted HRs. We then fitted interactions, with prespecified variables considered clinically important to investigate effect modification by current age ; time since index date ; and history of any CVD prior to index date (other than the CVD outcome of interest) for the coronary artery disease (composite), arrhythmia, stroke, pericarditis (USA only), HF, VHD and VTE (composite) outcomes (interactions were not investigated for other outcomes due to limited power). We statistically tested for interactions using likelihood ratio tests. Women with missing data  were excluded from all analyses , which is valid in a regression context if missingness is conditionally independent of the outcome.The main analyses were repeated with the UK and US study populations and covariates modified to be as similar as possible  initially prescribed tamoxifen and 5289 (53%) initially prescribed an AI ; the medIn both the UK and USA, there was a higher observed rate of most CVDs, excluding VTE outcomes, among those ever exposed to an AI compared with tamoxifen . In adjuThe US study included women without exposure to either endocrine therapy; observed rates of most CVDs, excluding VTE, were lower in both the tamoxifen and AI groups compared with the unexposed group, while VTE outcome rates were higher in the endocrine therapy groups . The patThere was no strong evidence of effect modification by age, time since index date or prior CVD in the UK or USA , though Following modification of study populations, methodology and covariates in the UK and USA to make them as similar as possible, the risk of all CVDs associated with ever AI compared with tamoxifen use were generally in the same direction . HoweverOur results suggest that higher observed risks of cardiovascular outcomes including coronary artery disease, MI, arrhythmia, HF, pericarditis and VHD in AI users compared with tamoxifen users are driven by protective associations of tamoxifen with CVD outcomes, rather than any cardiotoxic effects of AIs. These two population-based cohort studies using UK and US data are the first to apply similar methodology to two large populations to assess the effects of endocrine therapies on a range of CVD outcomes in postmenopausal women with breast cancer. Both countries’ results suggested a higher risk of several CVD outcomes in AI compared with tamoxifen users. However, when compared with patients receiving no endocrine therapy, there was no raised risk of any CVD outcome in users of either drug class, other than VTE in tamoxifen users. Furthermore, tamoxifen users had lower risks of coronary artery disease, MI, stroke, arrhythmia, pericarditis and VHD than unexposed women. This protective effect of tamoxifen might be explained by the drug’s effect on lipid levels; previous studies have found tamoxifen to reduce cholesterol.A recent meta-analysis of trials reported an increased risk of CVD, excluding VTE, in tamoxifen compared with AI users ,Most (5/6) previous studies directly comparing the risk of MI in AI and tamoxifen users have reported a higher risk in AI users, similar to our results (RRs ranged from 0.99 to 2.02).Six previous trials compared the risk of VTE in AI and tamoxifen users, with five reporting a lower risk in AI compared with tamoxifen users (RR ranged from 1.25 to 0.61), as reported in both UK and US data in this study.A major strength of this study was the use of two large data sources with complementary strengths and different limitations. We could look for consistency between countries and conduct different comparisons, notably between classes and with unexposed patients. We were also able to assess the relationships between endocrine therapy treatments and a wide range of CVD outcomes, rather than the composite and individual CVD outcomes in previous studies. We were able to account for several important confounders such as potentially cardiotoxic treatments (in US data) and lifestyle factors (in UK data), although no confounders materially changed the crude effect estimate when adjusted for individually in either the UK or USA . As the In the UK, ER/PR status was not available, but it is likely that breast cancers were ER/PR+ as such a diagnosis is a prerequisite of being prescribed endocrine therapies. Detail on cancer stage and administered therapies were also not available in the UK; although the cancer diagnosis is fed back from specialists to general practitioners, further detail is typically not recorded by the general practitioner and hence not available in the CPRD. HES also does not include these data; it is not a clinical care database and is rather derived from administrative data within the National Health Service. Furthermore, CPRD captures prescriptions at the point of issue, but we do not know if prescriptions were filled, which could lead to potential misclassification of exposure. However, descriptive analyses using a 3-month grace period to define a continuous prescription indicated that 94% of women continued to be prescribed within 1 year of starting, 85% within 3 years and 74% within 5 years.In the USA, the proportion of non-initiators of endocrine therapy (21%) was similar to the proportion of non-initiators reported in a previous study.Among postmenopausal women diagnosed with ER/PR+ breast cancer, we found convincing evidence of a higher risk of several CVDs in AI compared with tamoxifen users. However, our results indicated no excess cardiotoxicities of either drug class, other than the known raised risk of VTE with tamoxifen use. There was no evidence of raised risk of any specific CVD with AI use, including in stratified results restricted to those with other prior CVDs, suggesting that a history of CVD is unlikely to be an important contraindication for prescription of an AI. Our results contribute to the evidence base regarding the risk–benefit balance of endocrine therapies with respect to cancer survival and cardiovascular outcomes and ultimately can help identify subgroups of postmenopausal women who are likely to benefit from tamoxifen over AIs.Women with oestrogen or progesterone receptor positive breast cancer typically receive endocrine therapies, either tamoxifen or aromatase inhibitors (AIs), to reduce cancer recurrence risk. Recent studies have suggested raised cardiovascular risks in AI compared with tamoxifen users, but it is unclear whether this reflects cardiotoxicities of AIs or a protective effect of tamoxifen.Among postmenopausal women with breast cancer, we found a higher risk of several cardiovascular diseases in AI compared with tamoxifen users across two countries, which appeared to be driven by protective effects of tamoxifen, rather than toxic effects of AIs. We also found the known higher venous thromboembolism risk in tamoxifen users.Previous evidence could lead to reduced uptake of AIs among those at a high risk of cardiovascular disease. However, there is no current evidence to suggest that the cardiovascular benefits of tamoxifen outweigh the far superior effect that AIs have on breast cancer recurrence in this population, and there is no higher cardiovascular risk when prescribing AIs to these patients. These results should allay any concerns about postulated cardiotoxicities of AIs, and modifications to clinical practice could reduce the risk of patients being prescribed inappropriate endocrine therapies.


Studies suggest that adiposity in childhood may reduce the risk of breast cancer in later life. The biological mechanism underlying this effect is unclear but is likely to be independent of body size in adulthood. Using a Mendelian randomization framework, we investigate 18 hypothesised mediators of the protective effect of childhood adiposity on later-life breast cancer, including hormonal, reproductive, physical, and glycaemic traits. Our results indicate that, while most of the hypothesised mediators are affected by childhood adiposity, only IGF-1 (OR: 1.08 [1.03: 1.15]), testosterone , age at menopause (OR: 1.05 [1.03: 1.07]), and age at menarche  influence breast cancer risk. However, multivariable Mendelian randomization analysis shows that the protective effect of childhood body size remains unaffected when accounting for these traits (ORs: 0.59–0.67). This suggests that none of the investigated potential mediators strongly contribute to the protective effect of childhood adiposity on breast cancer risk individually. It is plausible, however, that several related traits could collectively mediate the effect when analysed together, and this work provides a compelling foundation for investigating other mediating pathways in future studies. Previous studies suggest that adiposity in childhood may reduce the risk of breast cancer in later life. A Mendelian randomization analysis suggests that the effects of adiposity may remain even when 18 potential mediators are accounted for, suggesting a lack of evidence for mediation and that other mediating pathways remain to be determined. Although breast cancer mortality has declined over recent decades, mostly due to improved treatments and personalised diagnoses3, the incidence of the disease has been steadily increasing by 3.1% annually since the 1980s4. Reducing the incidence rates will depend on better understanding and communicating modifiable risk factors both population-wide and targeted at women with an increased risk5.Breast cancer is the leading cause of cancer-related deaths among women, with 1 in 8 at risk of developing the disease in high-income countries6, which has been extensively studied in relation to breast cancer. In observational studies, there is consistent evidence for a positive association of increased body mass index (BMI) with post-menopausal breast cancer, but an inverse association with pre-menopausal breast cancer8. This may be explained by varying levels of exposure to oestrogen in overweight compared with normal-weight women. Pre-menopause, overweight women have longer anovulatory cycles, thereby decreasing their exposure to ovarian hormones which has been suggested to reduce their breast cancer risk9. Post-menopause, adipose tissue is the main source of oestrogen biosynthesis10. This increases exposure to oestrogen in overweight compared to normal-weight women, which may explain their higher risk of breast cancer after menopause.The second-largest preventable risk to all cancers after smoking is obesity13, several Mendelian randomization (MR) analyses have shown a contrary result, where genetically instrumented BMI is inversely associated with the risk of both pre- and post-menopausal breast cancer15. Since the genetic variants used to instrument BMI are set at birth, they should not be affected by environmental factors in later life. Thus, Guo et al.14 hypothesised that the positive association between high BMI and post-menopausal breast cancer risk seen observationally may reflect adiposity and weight gain later in life. This is supported by findings of an inverse association between early-life (childhood and adolescence) BMI with breast cancer risk12. A recent MR study16 using genetic variants related to childhood body size showed the same protective effect on breast cancer risk. Furthermore, using a multivariable MR analysis that accounted for adult body size, this study indicated that the protective effect of childhood body size influences breast cancer risk directly, independently of adult body size. However, the mechanism by which larger body size during childhood may reduce future breast cancer risk is not understood. Deciphering the mediating pathway between early-life adiposity and breast cancer would be of great interest for identifying targets of intervention, since advocating weight gain in childhood is not recommended.While previous conclusions have largely been drawn from observational epidemiological studies18). It has also been shown that incidence of breast cancer, particularly oestrogen receptor-positive (ER+) breast cancer, is substantially driven by changes in reproductive patterns, including parity and age at menarche, first birth and menopause20), events that are also influenced by increased adiposity in childhood22. Physical traits such as breast mammographic density (MD), which is an established risk factor of breast cancer23, are also affected by body fatness in early life25. Finally, glycaemic traits have been extensively studied in relation to BMI and, while they have been inconsistently associated with breast cancer28, are also of interest as potential intermediates.Many established breast cancer risk factors are plausible candidate mediators of the protective effect of early-life body size, since they have been found to be influenced or associated with childhood adiposity. For example, body fatness during childhood may protect against breast cancer risk through hormonal pathways  summary statistics for childhood body sizes47 Fig. . FirstlyFor each mediator, we conducted a two-step MR, where each step is an independent univariable two-sample MR analysis , as well as an effect on age at first birth (−0.09 −0.16: −0.03]). There was little evidence that age at menopause and number of births were affected by increased childhood body size . Age at menopause was found to have a positive effect on breast cancer (OR: 1.05 [1.03: 1.07]). The OR point estimates of other reproductive traits were inverse, but overall, there was little evidence of their effect on breast cancer . The estimates for age at menarche and age at menopause from the other data source were in agreement with the results in Fig. 6: −0.03)The tested glycaemic traits generally had strong evidence of being positively affected by increased childhood body size, although there were some inconsistent results using different data sources for the same traits  and the non-dense area had a negative effect (OR: 0.65 [0.46: 0.92]) on breast cancer risk. There was also a positive effect of percent MD, although with wider confidence intervals (OR: 1.43 [0.97: 2.12]).For the physical traits, we were only able to perform the second step of the MR analysis  since we did not have access to the full GWAS summary data. There was limited evidence of an effect from breast size on breast cancer risk (OR: 1.11 [0.72: 1.71]), as previously shown by Nick Sern Ooi et al.35  and negative (ER−) groupsWe next performed MVMR analyses with childhood body size and each mediator in turn, in relation to breast cancer risk Fig. .The direct effects of childhood body size on breast cancer after accounting for each mediator estimated using the IVW-MVMR method are presented in Fig. We also performed analyses with adult body size and childhood height as third exposures in MVMR (in addition to childhood body size and the mediator) in two separate analyses. Again, no substantial change in the direct effect of childhood body size was observed in the presence of the additional exposures , was addressed with the split-sample approach (see Methods). Results of this analysis were consistent with the full sample analysis , suggesting potential mediation via this trait. The estimated proportion of the mediated effect via IGF-1 was 0.039, indicating that any potential mediation via IGF-1 would only account for 3.9% of the total effect. Conversely, the alternative mediation approach (Difference method) estimated the IGF-1 indirect effect to be positive , and inconsistent across mediation methods. While there was some evidence for indirect effects of childhood body size via age at menarche, as previously reported57, and testosterone levels, this was in the positive direction in opposition to the total inverse effect of childhood body size on breast cancer. Lastly, it was not feasible to confidently assess the effect of oestradiol on breast cancer in MR analyses due to the limited number of genetic instruments in the available data, but as oestradiol has been observationally associated with breast cancer18, its mediating role remains of interest.The results of our MR analysis of selected hormones on breast cancer are supported by recent MR studies, with similar effects observed for IGF-1 in Murphy et al.We also reviewed the effects in ER− and ER+ cancer samples, which can proxy for younger/older or pre-/post-menopausal women, respectively. The observed causal effects for hormones were maintained in the ER+ samples, but no effect was observed in the ER− samples. Some differences were also observed for reproductive traits, for example, age at first birth had a direct effect only on the ER− cases (younger women). Additionally, there was a null effect of age at menopause and increased number of births among ER− breast cancer cases, which is reasonable since those exposures are less prevalent in younger women who typically present with ER− breast tumours.58.Although we used the largest GWAS available for each mediator trait of interest, many of these data sources possess specific limitations that may have prevented us from identifying the mediated effect. When investigating hormones as outcomes, we used data from UK Biobank, which is the same sample as our main exposure (childhood body size). To assess whether sample overlap could have potentially led to winner’s curse bias in step 1 of MR, a split-sample analysis was performed. Results of non-overlapping samples analysis were similar to the full sample analysis, suggesting that using the same data source for both exposure and outcome had little impact on our findings. Another limitation related to the hormones measures is that these were quantified in a predominantly post-menopausal sample of women (average age in UK Biobank is 56 years), where sex hormone levels are considerably different to those before menopauseFor the reproductive traits, we prioritised non-UK Biobank datasets in the main analysis to minimise the problem of sample overlap. While these datasets typically were from smaller sample sizes (and therefore fewer instruments), the directions of observed effects were consistent with the analyses performed on UK Biobank data  were validated in several different cohorts64 to be able to reliably separate childhood and adult body size, and also were robust to differential measurement error in simulations performed in the original study. Collectively, these analyses confirm the genetic variants from this data are suitable to be used to represent childhood body size.Childhood body size data from UK Biobank is based on a questionnaire completed by adult participants and could be subject to misclassification due to individuals misremembering their relative body size, which could potentially pose a great limitation to using this data. However, the genetic variants for childhood body size  to determine if early-life and adult adiposity have different effects on breast biology, which would be a critical step in deciphering the protective effect investigated in this work.67, aiming to identify the mediators of height effect on coronary artery disease). While we adopted a hypothesis-driven approach to investigate potential mediators, in future work, data mining platforms such as EpiGraphDB (epigraphdb.org)68 may be used to facilitate the identification of novel mediator traits/biomarkers, or candidates for multi-mediator MVMR analyses.Our systematic investigation of mediators was designed as a prioritisation workflow, in which the initial MR analysis results (two-step MR) were used to select mediators for more advanced analyses  based on the presence of causal effects and adequate performance in sensitivity analyses. Although we failed to identify a sole plausible mediator, we systematically report the MR results for the majority of obvious candidates from the largest available GWAS datasets to our knowledge. As new GWAS data becomes available, a similar approach can be applied, or used for investigating other biological questions  from the Breast Cancer Association Consortium (BCAC)35. The sources of mediator traits GWAS are summarised in Table In this study, the main exposure trait GWAS (childhood body size) was from UK Biobank69. The GWAS of childhood body size and adult body size used in this study were performed by Richardson et al.16 on UK Biobank data .UK Biobank is a population-based health research resource consisting of approximately 500,000 people, aged between 40–69 years, who were recruited between the years 2006 and 2010 from across the UK. UK Biobank has received ethical approval from the UK National Health Service’s National Research Ethics Service (ref 11/NW/0382) and informed consent from all participants. A full description of the study design, participants and quality control (QC) methods have been described in detail previouslyN = 69,501) and oestrogen receptor-negative  participants35. The details of cohort design and genotyping protocol are described elsewhere . The study groups in the BCAC cohort do not include UK Biobank or mediator trait cohorts. The BCAC data was accessed through OpenGWAS36 (gwas.mrcieu.ac.uk) under the following IDs: ieu-a-1126 (full sample), ieu-a-1127 (ER+), ieu-a-1128 (ER−).BCAC breast cancer GWAS includes 228,951 samples  of European ancestry. The cases include both oestrogen receptor-positive 63 and Young Finns Study64), which confirmed that the genetic instruments extracted by Richardson et al.16 can reliably separate childhood and adult body size as distinct exposures.Childhood body size is a categorical trait describing body size at age 10, with three categories , from a questionnaire completed by adult participants of UK Biobank. Adult body size measure was converted from continuous adult BMI in UK biobank into three groups based on the proportions of childhood body size data to ensure that the GWAS results of both measures are comparableChildhood height is another categorial trait from a questionnaire completed by adult UK Biobank participants, with three categories describing comparative height at age 10 .52. The GWAS data for these traits was generated as a part of this study using the IEU GWAS pipeline (next section), and the consistency of genetic instruments was validated using LD Score Regression70 with the published analyses of the same traits (Supplementary Note Hormones: IGF-1 (insulin-like growth factor 1), SHBG (sex hormone-binding globulin), oestradiol, testosterone . The different measures of testosterone were estimated as described in previous workReproductive traits: age at first birth, age at menarche (×2), number of births, age at menopause (×2).Glycaemic traits: fasting insulin (×3), fasting glucose (×3), HbA1c (glycated haemoglobin A1c) (×2), HOMA-B (Homeostatic Model Assessment of β-cell function). HOMA-IR (insulin resistance) was considered in the analysis too, however, no robust instruments were identified.Physical traits: breast size (×3), MD  (×3). Full summary data was not available for breast size and MD, so only GWAS top hits were used in the analysis.Four groups of mediators were assessed: sex hormones, female reproductive traits, glycaemic traits, and physical traits Table .HormonesSeveral traits were available from multiple data sources (marked with ×N). In the early stages of the analysis, all of them were evaluated, but only one version is presented in the final set of results. To prioritise a particular dataset over the rest we used the following criteria: 1) female-only sample, (2) non-UK Biobank data, (3) sample size. Table  female-o71 linear mixed model and an additive genetic model adjusted for sex, genotyping array, and 10 genetic principal components. The data were inverse rank normalised prior to the analysis; the results are quantified as standard deviation change.The GWAS of hormone mediators from UK Biobank were performed using the MRC-IEU GWAS pipeline which is based on BOLT-LMM (v2.3)29. There are three core assumptions that genetic variants need to satisfy to qualify as valid instruments for the causal inference: (1) variants have to be reliably associated with exposure of interest, (2) there cannot be any association with confounders affecting the exposure-outcome relationship, and (3) variants cannot be independently associated with the outcome, via pathway other than through the exposure 72.MR is a type of instrumental variable (IV) analysis where genetic variants are used as proxies to uncover the causal relationship between a modifiable health exposure and a disease outcome73. Two-sample MR is the basis for the more advanced analysis setup described in the next sections.The analyses in this work were performed using the two-sample (univariable) MR approach  .Two-sample MR analyses were performed using the inverse-variance weighted (IVW) method75. As shown in Fig. 75. The evidence of a causal effect in both steps suggests that the association between exposure and outcome is mediated by the intermediate variable to some extent .Two-step MR  is a sequence of two (or more) MR analyses connected by a shared variable. Two-step analysis setup is used to assess whether an intermediate trait acts as a causal mediator between the main exposure and the outcome of interest33. In this way, an exposure trait and a potential mediator can be analysed together to quantify the direct effect of both independently on the outcome  is an extension to the standard univariable MR, which allows genetic variants to be associated with more than one exposure, and can estimate the direct causal effects of each exposure in a single analysis78, which was also used for accessing GWAS summary data deposited in OpenGWAS36 (gwas.mrcieu.ac.uk). Multivariable MR was carried out by adapting TwoSampleMR’s functionality to be used on mixed data sources (see “Code availability”). Sensitivity analyses for multivariable MR were performed using MVMR R package (version 0.2)34.All analyses were conducted using R (version 4.0.0). Univariate MR analyses and sensitivity tests were performed using the TwoSampleMR R package (version 0.5.4)P value < 5 × 10−8 threshold and clumping the resulting set of variants with r2 < 0.001. When extracting instruments from the outcome (breast cancer) GWAS summary statistics, unavailable SNPs were substituted by proxies with a minimum linkage disequilibrium r2 = 0.8. The rest of the settings were kept to defaults as per package version number.For all exposure and mediator datasets, the instruments were extracted from the full summary data by selecting SNPs under 49 and weighted median MR50 methods were used to overcome and accommodate for potential violations of the core MR assumptions. These complementary methods help to support the causal effects found with IVW, as a single method cannot account for all biological and statistical properties that may impact MR estimates. A variety of recommended78 specialised sensitivity tests was also applied.To further investigate the causal estimates found in the standard (IVW) MR analyses and to evaluate the validity of the analysed genetic instruments, MR-Egger49 was evaluated, and the heterogeneity among the genetic variants was quantified using Cochran’s Q-statistic79. The intercept term in MR-Egger regression is a useful indication of whether directional horizontal pleiotropy is driving the results of an MR analysis. When the Egger intercept is close to zero  and the P-value is large, this can be interpreted as no evidence of a substantial directional  pleiotropic effect. When the Q-statistic for heterogeneity  is high and the corresponding p-value is small, this suggests evidence for heterogeneity and possibly horizontal pleiotropy. A high Q-statistic can be also used as an indicator of one or more variant outliers in the analysis, which may also be violating the MR assumptions.To assess overall horizontal pleiotropy (violation of assumption 3), the intercept in the MR-Egger regressionAdditionally, scatter plots of SNP effects from exposure and outcome fitted by all tested MR methods were evaluated for any deviations which would also be indicative of heterogeneity and violations of MR assumptions. Single SNP forest plots were used to summarise the effect of the exposure on the outcome due to each SNP separately, which is a helpful approach for visualising SNPs heterogeneity. Next, funnel plots were used to visually evaluate the direction of pleiotropy, which, if present, would be characterised by asymmetry in the plot. Finally, the sensitivity of causal inference to any individual genetic variant was tested by leave-one-out analysis, which is used to identify outliers.76, with F > 10 indicating suitable strength for the analysis. However, as in univariable MR, heterogeneity may be indicative of horizontal pleiotropy that does not act through one of the exposures. In MVMR, heterogeneity is quantified by QA-statistic , and small QA indicates a lack of directional pleiotropic effect76.In MVMR analyses, conditional F-statistics were used to evaluate the instrument strength80 R package. This is an alternative approach to using individual-level data for matrix construction. The method was only applied in cases when both exposures in MVMR were from the same sample . When data samples were different, the default settings for F and QA statistics were used 76.To calculate both statistic measures, a phenotypic correlation matrix for each MVMR test had to be constructed. This was done by applying the method for phenotypic matrix construction from GWAS summary data, available from metaCCA76.In the cases where MVMR sensitivity tests indicated the presence of weak instruments and potential pleiotropy via heterogeneity, Q-minimisation approach (Q-het) for estimating causal association was used to supplement the estimated of MVMR-IVW approach82. In the two-sample MR analyses of childhood body size on hormones and some of the reproductive traits, this requirement was not satisfied, since the mediator GWASs were performed in the same cohort as the main exposure (childhood body size), i.e., UK Biobank.One of the important requirements in two-sample MR is that the exposure and outcome GWAS are two non-overlapping datasets, which provides an advantage over the limitations in one-sample MR analysis of winner’s curse and anti-conservative weak instrument biasp < 5 × 10−8), and step 1 of two-step MR and MVMR were repeated, with the resulting estimates are available in Supplementary Data 34.To overcome the bias and evaluate the extent to which the results are affected, the MR analyses were repeated using a split-sample approach. This was possible due to the large sample size of the UK Biobank: the data were randomly split into two parts, and one part was used to carry out a new GWAS for the exposure (childhood body size), and the other part for the outcome (hormones/reproductive traits). The exposure instruments were extracted from the new split-sample exposure GWAS In terms of MR, the total effect is captured by a standard univariable MR analysis Fig. . For med84, it has been shown that if the outcome effects have been quantified as log-odds ratios, it is acceptable to use them for estimating the indirect effects66. However, it is important to note that the analysis on log-odds ratios from both MVMR and two-step MR is likely to have some bias for both rare and common binary outcomes66. When the outcome is common, like in the present study (53.7% cases), is it expected that the estimates from Product and Difference methods are not going to align, and both are likely to be biased, with Difference method producing a conservative estimate85.Lastly, although it is difficult to perform mediation analysis on binary outcomes  due to the non-collapsibility of odds ratiosUnsurprisingly, we observed a disagreement between the estimates from the two methods in the results and therefore performed simulation analysis to help us choose the method that produces more reliable results in our study. The mediation results and simulation analysis are described in Supplementary Note Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1-20Reporting Summary


This cross-sectional study examines knowledge, attitudes, and practices surrounding breast cancer awareness and screening among women residents in Qatar. Females, >18 years old, registered with the Primary Health Care Corporation were invited to complete an Arabic or English online survey using a modified version of the Breast Cancer Awareness Module. Of the 9008 participants, 69% report awareness of breast cancer warning signs, but the results did not substantiate these claims. There remains a disconnect between participants’ perceived awareness of their ability to detect breast cancer and their actual recognition of individual signs and symptoms. Nearly half (45.4%) report rarely or never checking their breasts for abnormalities (44.6%). Breast self-examination (BSE) and Breast Cancer Screening (BCS) uptake is low and many are unaware of the starting age for invitation to Qatar’s BCS program. While only 18% of women report receiving an invitation, 94% attended, indicating that the BCS invitation is a remarkably effective means of improving screening uptake. Policymakers should capitalize on early recognition, which is possible in the youthful population. Broadening awareness campaigns and interventions targeting a broader audience including males, community and religious leaders and healthcare professionals may prove more effective in Arab communities. Breast cancer is a life-threatening disease affecting women across the globe . It is tIn the Middle East, breast cancer is the most prevalent cancer among females . In QataEarly detection of breast cancer is associated with decreased age-adjusted mortality rates of the disease . Women wOne of the Gulf Cooperation Council states, the state of Qatar offers free universal health care to its residents and citizens. Although several private clinics and hospitals operate in the country, approximately 70% of the population is registered with and receives primary care via the Primary Health Care Corporation (PHCC) . Hamad MHowever, despite the coordinated efforts of these organizations, community knowledge of the National Breast Screening Program (NBCS) remains suboptimal. The majority of women who participated in the study indicated they neither know the starting age for invitation for the NBCS program  nor the age women receive their last invitation . Only 18% of respondents acknowledged that they received an invitation for breast screening  and of those, 17% subsequently attended the NBCS . These figures suggest there is scope for improvement regarding public awareness of breast screening.Over the past decade, an increasing number of studies have examined Arab women’s knowledge about the signs and symptoms of breast cancer and the risk factors of the disease ,18,19,20A list of mobile phone numbers for all women over 18 years of age and registered with a PHCC health center was extracted from the PHCC’s electronic medical record system. A Short Message Service (SMS) message in Arabic and English was sent to 440,350 women’s mobiles inviting them to participate in the study in March–April 2021. The SMS included a link to a questionnaire hosted on Microsoft Forms. The first page of the questionnaire included a participant information form and an informed consent sheet, which required a signature prior to participation in the survey. The questionnaire administered was a modified version of the Breast Cancer Awareness Module (Breast-CAM) (The Breast Module of the Cancer Awareness Measure (Breast-CAM)* was developed by Cancer Research UK, King’s College London, and University College London in 2009 and was validated with the support of Breast Cancer Care and Breakthrough Breast Cancer) developed by Cancer Research UK, King’s College London and University College London in 2009 and validated with the support of Breast Cancer Care and Breakthrough Breast Cancer. The tool was back-translated, reviewed by native Arabic and English speaking researchers, and piloted among a small cohort of patients before dissemination. The questionnaire remained open for completion for four weeks. A reminder SMS was sent after 2 weeks.All data were collated and subject to quality assurance. The data were reviewed and cleaned before they were analyzed. Data were collected followed organizational safeguarding policies including encrypted storge and anonymous analysis. Descriptive statistics (frequency) and chi-squared tests were performed to determine any associations between categorical dependents and predictors. Statistical significance was established at alpha −0.05. Data analysis was conducted using SPSS version 25.The study was reviewed and approved by Primary Health Center Corporation’s Research Sub-Committee (PHCC/DCR/2020/09/108) and Qatar University (QU-IRB 1519-EA/21). Informed consent was obtained online. No participant identifiable information was collected. Only EH and MS had access to the study data. Overall, the study was conducted with integrity according to generally accepted ethical principles.The comprehensive online survey yielded a response rate of 2.04%. Of the 9008 women who participated in the survey, more than half were under 40 years of age (56%). Arabic was the predominant spoken language. The most common nationalities who participated were Qatari and North African, both at 20%. The majority of respondents (83%) were educated to the college level diploma or above. More than two-thirds of respondents purported an awareness of the warning signs of breast cancer 69% .When completing the survey, women could select more than one response. The most commonly recognized sign amongst the participants was a lump or thickening under the armpit , followed by a lump in the breast or thickening of the breast tissue . Other symptoms and warning signs were not recognized by more than half of the respondents. More than two-thirds of the respondents correctly identified “Past medical history of breast cancer” and “having a close relative with breast cancer” as risk factors for breast cancer. However, early menarche, late menopause, and advanced childbearing age were not readily recognized as risk factors by the survey cohort .Half of the respondents refrained from selecting a specific age group contending that women of any age were likely to get breast cancer , nor was there any certainty expressed about the incidence rate of women who will develop breast cancer in their lifetime .Nearly half of the respondents admit to rarely or never checking their breasts for abnormalities . Of those participants that do perform SBE: a fifth only check at least once every 6 months ; a similar number contend checking at least once a month  and a minority indicated they do so at least once a week . While a minority felt very confident in noticing a change in the breast , nearly a fifth of the survey responses reported they were not confident . Nearly a third of the respondents indicated that they saw a doctor about a change they have noticed in one of their breasts , and more than half indicated that upon detection of an abnormality, they would contact their doctor as soon as possible  .In general, women conveyed the sense that they found healthcare provision accessible and doctors approachable, harboring little fear of embarrassment (70%), concerns about wasting physician time (81%), or experiencing any difficulties communicating with their doctors (72%). The most common barrier to seeking medical help reported was difficulty making appointments with the doctor (60%). Half of the women cited sometimes or often being too busy to make time to go to the doctor (49%). Despite a majority claiming not being “too scared to go and see the doctor” and seeking medical help, just under half of the respondents  admitted “being worried about what the doctor might find.” Thus, the specific fear of diagnosis was a significant deterrent to health-seeking behavior .This study examined knowledge of symptoms and warning signs, awareness of risk factors of breast cancer, and barriers to seeking medical help amongst women in Qatar. The survey included women from all nationalities and sociocultural and sociodemographic backgrounds. To the best of our knowledge, no other study has used a validated measure to assess cancer knowledge, attitudes, and practices in Qatar. The results of BCS practices captured in this 2021 survey are consistent with earlier reported studies and results from other countries in the Gulf and Middle East ,21,22,23n = 1786) completing the survey identified as Qatari females; consequently, it is likely that a higher proportion of the survey population are Qatari than in the whole population of Qatar. The fact that Qataris were more likely to respond is also mirrored in their uptake of screening services.While Qatari nationals represent only 10% of the total population, 20% of women (Despite the availability of free health care services and a recent national awareness campaign in 2020 focusing on signs and symptoms, risk factors, prevention, and screening of breast cancer, the majority of women continue to have low awareness of signs and symptoms, risk factors, and the current screening program. This concurs with Donnelly et al.’s 2014 findings, which found women’s knowledge of national guidelines for breast screening practices in Qatar to be low . This stOf the positive risk factors of breast cancer, only two were readily identified: having a past history of breast cancer and having a close relative with breast cancer. The remaining six risk factors prompted the majority of women to declare that they were “not sure.” There appears to be much uncertainty about the link of using hormone replacement therapy and the consumption of alcohol. Qatar, being a Muslim-majority nation, might account for the lack of knowledge pertaining to the impact of drinking alcohol as this practice is prohibited in Islam.Many studies correlate that higher education level is a strong predictor for BCS awareness, yet the findings of our highly educated cohort contravene this assertion ,17. OverIn Qatar, an earlier study reported rates of BSE at least once a month of 13.8% and BCS programs in Qatar as low as 4% . Though Barriers to attending the BCS program differed from earlier work reflecting the change of demographics. In our population, time, availability, and accessibility are reported as significant barriers compared to health beliefs. Earlier studies in the Qatari society reported health beliefs and fear of the findings as more common ,18,24. URegion was the factor that had the most significant impact on breast self-examination. Women from Qatar, Africa, and Asia practice BSE once a month or less frequently compared to women from the Americas, Europe, and Australia. This result may highlight areas for improvement with a need for campaigns for breast cancer awareness and BSE in those groups who do not engage is BSE.Donnelly et al.’s study of female Arabic-speaking residents of Qatar concluded that nationality did not affect participation rates in BCS activities, attributing this to the fact that screening services are free or heavily subsidized . Our stuThough figures are unavailable, it is also possible that women are engaging in BCS in private clinics or abroad, neither of which would be captured in the survey disseminated by the PHCC, so the uptake of screening may be higher than reported here. These clinics may be perceived to afford more privacy and circumvent any stigma associated with breast cancer diagnosis that might occur in a small, closely-related community. Prior to the establishment of national screening programs and cancer treatment being readily available in Qatar, citizens were funded to travel abroad for diagnosis and treatment. A legacy of this practice may be that it has undermined confidence in local healthcare provision. Advertising the high caliber of expertise and advanced screening technology available in Qatar as well as celebrating local advances in breast cancer prevention and care could mitigate some of these issues.The Health Belief Model holds that a patient’s willingness to change health behaviors is contingent on their perceived susceptibility and severity as well as the perceived benefits and barriers associated with a particular health-seeking behavior. Uptake of screening occurs when a person feels the risk of disease is significant for them. It is likely, for instance, that the perception of low susceptibility to breast cancer accounts for why women under 39 years of age rarely or never perform SBE. Further education is needed to alert females to the signs and symptoms, and risk factors, in developing breast cancer so they can make informed decisions about engaging with breast screening from an early age. Amin et al. suggest that Arab patients tend to visit physicians only upon the onset of illness or the appearance of symptoms . This obDaher-Nashif and Bawadi found that in the GCC particularly, women’s health in general is shaped by the sociocultural factors that also shape health policies and institutional practices . ReportiThese baseline data demonstrate a persistent lack of awareness about BSE and an underutilization of screening services in Qatar despite recent breast cancer campaigns. Information communicated by healthcare providers is a strong predictor of BCS awareness . Though The high rate of responses indicate that the modified Breast-CAM is a culturally acceptable tool among the different Arabic and English-speaking ethnic groups in Qatar and is a useful mechanism for generating a comprehensive assessment of breast cancer awareness in a Middle Eastern context. Used regularly, this tool could provide a useful measure of changes in attitude and behaviors surrounding breast health.This survey was administered only in Arabic and English; consequently, results may not represent the minority population who are non-Arabic or non-English speakers or are illiterate. Similarly, individuals who prefer private healthcare and do not register with the PHCC will not be represented in this survey. Another factor that may influence the responses obtained is the familiarity expatriate females have with the Qatar health care system. The survey did not identify the number of years spent in Qatar at the time of completion of the survey, so consequently, a limitation of the interpretation arises since the information the participants have may be influenced by the strength of awareness of breast cancer campaigns from their country of origin. This is particularly relevant for new arrivals to Qatar.The current study findings imply an improvement in the knowledge and practices of BCS in Qatar, although there is significant room for improvement. There is a disconnect between participants’ perceived awareness of their ability to detect breast cancer and their actual recognition of individual signs and symptoms of breast cancer. Similarly, their BSE and BCS uptake is low. BCS invitation is remarkably effective in improving the uptake. Policymakers should consider the employment of the intervention for the national program and capitalize on as early recognition as possible in the youthful population. Awareness campaigns and interventions targeting a broader audience including males, community and religious leaders, and healthcare professionals may prove more effective in Arab communities. Further qualitative research should be undertaken to determine what factors are inhibiting the acquisition and subsequent application of SBE and CBE in these highly educated women.


Since 2000, the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET) modeling teams have developed and applied microsimulation and statistical models of breast cancer. Here, we illustrate the use of collaborative breast cancer multilevel systems modeling in CISNET to demonstrate the flexibility of systems modeling to address important clinical and policy-relevant questions. Challenges and opportunities of future systems modeling are also summarized. The 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to affect the burden of breast cancer. Multidisciplinary modeling teams have explored alternative representations of breast cancer to reveal insights into breast cancer natural history, including the role of overdiagnosis and race differences in tumor characteristics. The models have been used to compare strategies for improving the balance of benefits and harms of breast cancer screening based on personal risk factors, including age, breast density, polygenic risk, and history of Down syndrome or a history of childhood cancer. The models have also provided evidence to support the delivery of care by simulating outcomes following clinical decisions about breast cancer treatment and estimating the relative impact of screening and treatment on the United States population. The insights provided by the CISNET breast cancer multilevel modeling efforts have informed policy and clinical guidelines. The 20 years of CISNET modeling experience has highlighted opportunities and challenges to expanding the impact of systems modeling. Moving forward, CISNET research will continue to use systems modeling to address cancer control issues, including modeling structural inequities affecting racial disparities in the burden of breast cancer. Future work will also leverage the lessons from team science, expand resource sharing, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts. Since 2000, our research teams have used computer models of breast cancer to address important clinical and policy-relevant questions as part of the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET). Our 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to represent the burden of breast cancer. We have used our models to investigate questions related to breast cancer biology, compare strategies to improve the balance of benefits and harms of screening mammography, and support insights into the delivery of care by modeling outcomes following clinical decisions about breast cancer treatment. Moving forward, our research will continue to use systems modeling to address issues related to reducing the burden of breast cancer including modeling structural inequities affecting racial disparities. Our future work will also leverage lessons from engaging multidisciplinary scientific teams, expand efforts to share modeling resources with other researchers, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts. The purpose of systems epidemiology is to draw on expertise from different disciplines to enhance understanding of factors influencing health and disease and to use that knowledge to evaluate interventions for improving health outcomes –3. SysteSystems modeling provides a flexible epidemiologic method to address a variety of policy and clinically relevant questions in cancer control. Since 2000, the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET) modeling teams have developed and applied microsimulation and statistical models of several types of cancer, including breast cancer . CISNET Here, we illustrate the use of collaborative breast cancer modeling in CISNET as a systems epidemiology method. Examples are provided that demonstrate the key features of systems modeling. We also provide recommendations for future team science efforts to develop and apply systems modeling to address policy-relevant cancer prevention and control questions. This article is intended to illustrate the potential of systems modeling and highlight challenges and opportunities for systems modeling to contribute to meeting national cancer goals.No personally identifiable information was used in this study or the modeling research summarized herein. The University of Wisconsin Health Sciences Institutional Review Board determined that this study was not human subjects research (Protocol #2020–0620).The CISNET Breast Working Group includes 6 multilevel microsimulation or analytic models: Model D (Dana Farber Cancer Institute) , Model EThe tumor, individual person (woman), and health system levels in the models interact to provide insight into the factors affecting breast cancer incidence, mortality, and other health outcomes. The models incorporate estimates of age-specific breast cancer incidence and estrogen receptor (ER)/human epidermal growth factor receptor 2 (HER2) subtype-specific survival trends in the absence of screening or treatment and then incorporate information on how screening and molecular subtype-specific treatment patterns affect the underlying trends. Based on age-specific performance characteristics, screen detection during the preclinical screen-detectable period can result in diagnosis of earlier-stage or smaller tumors than diagnosed via symptomatic detection. Regardless of method of tumor detection, women diagnosed with breast cancer receive treatment specific to the molecular subtype of their tumors and their age at diagnosis .Table 1 with additional details available in prior publications  at age 40 . Women aAlthough women are assigned receipt of screening or therapy at the individual person level in the models, these interventions are delivered via the health system. Hence, healthcare policies at the health system level heavily influence breast cancer screening and treatment that women receive. Women are assigned an age at first mammogram and screening frequency based on the distribution observed for their birth cohort using data from the BCSC, the National Health Interview Survey, and the US Food and Drug Administration’s Mammography Quality Standards Act and Program . Model iAll women diagnosed with breast cancer are assumed to receive initial therapy with mastectomy or lumpectomy with radiation, but local therapy is not explicitly modeled. Subtype-specific adjuvant treatment  is assigned according to a dissemination model based on SEER patterns of care special studies 1980 to 1996) and the National Comprehensive Cancer Network Outcomes Database (1997 to 2012) . Breast 0 to 1996Table 2).The population of US women is modeled starting in the year 1975 until the most recent year of data available in the SEER Program. Either the entire population can be modeled  or a single recent birth cohort can be selected to simulate expected outcomes of a contemporary cohort of women receiving the current standard of care for breast cancer screening and treatment. The models generate a wide range of benefit and harm outcomes  .As an exercise to validate models against data that were not used in their construction and calibration, we have replicated results of the United Kingdom Age Trial of mammography screening for women in their 40s. The results demonstrated that the models closely reproduced the effect of annual mammography screening on breast cancer incidence and long-term mortality . To captPrevious studies have demonstrated the flexibility of the CISNET breast cancer models to incorporate the complex interplay between the multiple factors on breast cancer as reflected by tumor, person, and health system levels. Here, we summarize exemplar findings by the CISNET breast cancer researchers that demonstrate these interactions.Fig 1). For example, for ER-negative/HER2-negative cases, the relative contributions by treatment were always lower than those by screening reflecting the lack of effective treatments for this subtype.The number of treatment options for women diagnosed with breast cancer has increased dramatically over the past 2 decades. The CISNET breast cancer modelers have quantified the associations of screening and treatment with reductions in US breast cancer mortality rates. Recent work extended a previous analysis  by updatCISNET modeling teams have also examined linkages between the tumor and health system level interactions related to the impact of radiotherapy by simulating a clinical trial. This research conducted by Models GE and M illustrated that simulation modeling can prove useful in the modification or redesign of clinical trials. This analysis found that omission of radiotherapy in hormone-sensitive patients with low recurrence risk may lead to a modest increase in locoregional recurrence event rates, but did not appear to increase the rate of distant recurrence or death ,38. ModeAs another example of the interaction between the natural history of breast cancer represented by tumor growth and the health system in the CISNET models, we have investigated several issues focused on ductal carcinoma in situ (DCIS). Following the widespread dissemination of mammography screening for breast cancer, DCIS incidence increased sharply . While oTwo CISNET models (Models D and E) investigated the natural history of DCIS and selected 6 plausible parameterizations that could explain DCIS and invasive breast cancer incidence in the US. Using the models, they estimated mean sojourn time in the preclinical screen-detectable DCIS state, overdiagnosis of DCIS, and progression to invasive breast cancer in the absence of biopsy or complete excision . OverdiaThe modeling work on DCIS shows the challenges to deciding between alternative representations of DCIS natural history and the complexity of healthcare decisions for DCIS. Main findings from Models D and E indicate that the majority of screen-detectable but unbiopsied preclinical DCIS lesions progress to invasive breast cancer and that the mean sojourn time in preclinical DCIS is relatively short. Given the heterogeneous nature of DCIS, the progression of DCIS needs to be further modeled by grade and molecular subtype to more completely reflect the key features of DCIS biology. Overall, this body of work illustrates that screening approaches for breast cancer selectively detect different tumor characteristics, and CISNET models are well suited for representing the interplay between tumor and detection features in healthcare delivery.Table 3). Overall, we found that using the person-level factors breast cancer family history and polygenic risk to tailor (system level) screening strategies, more breast cancer deaths can be prevented and lives extended [In CISNET, we have examined several instances where person-level factors interact with health system level factors to influence breast cancer outcomes. In a first example of person and health system level interactions, we used 2 CISNET models (Models E and GE) to evaluate the benefits and harms of mammography screening based on polygenic risk  and famextended . In geneTable 4). Person-level differences in terms of risk of breast cancer and life expectancy between women with and without Down syndrome implied that health system level policies for starting and ending ages of mammography screening may need to be set differently for these women.In another example of person and health system level interactions, we used two of the CISNET models (Models E and WH) to evaluate the benefits and harms of mammography screening for women with Down syndrome, who have unique personal characteristics related to breast cancer outcomes . Women wBRCA1 mutation carriers [Similarly, we used 2 CISNET models (Models GE and WH) to evaluate the clinical benefits and harms associated with screening guidelines for a group with unique personal characteristics . Survivocarriers . These wcarriers  This comcarriers , adherenFig 2). Importantly, we found that MRI accounted for the majority of the screening benefit. Our findings highlight the importance of reducing barriers to MRI screening for these high-risk women and underscore the need to identify other options for breast cancer prevention as some survivors may prefer mammography and/or seek to reduce their risk even further. In future modeling research, we will be extending this work to evaluate the impact of healthcare system level factors such as physician recommendations for primary prevention with hormonal therapy on screening strategies and outcomes.To estimate the clinical outcomes associated with screening among these women, we adapted the models using data from the Childhood Cancer Survivor Study  to refleTable 1). In certain focused analyses, risk of breast cancer incidence, and thus tumor inception for models with natural history components, depends on breast density. In these analyses, sensitivity and specificity of screening mammography also vary by breast density in addition to age, screening round (first versus subsequent), and screening frequency. For example, in an analysis parallel with modeling conducted for the USPSTF [Fig 3) [Modeling of 2 factors—age and breast density—are an example of modeling that incorporates interactions across all 3 levels of factors that affect breast cancer outcomes. Breast density in the CISNET models is assigned to each woman at the time she begins receiving mammography screening. As described above, density is assigned to women in 4 values (reflecting lower to higher density) and 3 age groups  .Findings demonstrated how, for average-risk women, moving from screening mammography every 3 years to every 2 years increases the harm/benefit ratio—as measured by false-positive mammograms per death averted—less than moving from biennial to annual screening. This harm/benefit ratio is higher for women with scattered fibroglandular and heterogeneously dense breasts than for women with almost entirely fatty or extremely dense breasts. For older women, this harm/benefit ratio is essentially equivalent for biennial and triennial screening. As screening performance improves with technological advances, such as digital breast tomosynthesis , the CISThe CISNET Breast Working Group has used multilevel systems modeling over the past 20 years to provide evidence to guide high priority public health, clinical, and individual decisions to reduce the burden of cancer. Models incorporate key features of tumors to address questions related to the biology of breast cancer and its relation to the different approaches for early detection. Person-level characteristics such as age, family history of breast cancer, polygenic risk, and breast density have also been evaluated to elucidate screening strategies that improve the balance of benefits, harms, and costs of screening. As an extension of our work focused on women of average risk, we have also examined screening and treatment approaches for special populations including survivors of childhood cancer and women with Down syndrome.The models include the interactions between breast cancer epidemiology trends, evolution in screening technology, advances in breast cancer therapy, and new insights into the heterogeneous biology of each molecular subtype of breast cancer. The models incorporate various data types, structures and sources to reflect the tumor, person, and health system levels of data needed to portray the complexity of this common disease. As the CISNET Consortium continues its efforts to interrogate the pressing issues related to breast cancer incidence, detection, treatment and survival, additional challenges and opportunities remain to increase the impact of the consortium.Racial disparities in breast cancer outcomes have persisted for decades. These disparities are substantial, including the 40% greater breast cancer mortality experienced by Black compared with White women . SimulatCISNET models have been used to quantify the impact of breast cancer natural history, screening, and adjuvant treatment on mortality disparities . This woRecent CISNET work has begun to develop updated race-specific subtype-specific parameters and incorporate modern data on treatment disparities, enhancing the ability of CISNET models to account for present-day disparities. Future refinement of CISNET models will permit more precise attribution of mediating factors, including disparities in stage at diagnosis, screening receipt, and treatment receipt and timeliness. Enhanced modeling of mediators will, in turn, help identify the highest yield solutions for reducing disparities. Indeed, recent model updates have permitted identification of equitable screening strategies for Black women. The results of a recent CISNET study using updated model inputs demonstrated that screening may need to be initiated for Black women 5 to 10 years earlier than for White women (age 40 or 45 versus 50) to derive similar health benefits . This stPrimary breast cancer prevention interventions have been underutilized –62, and Screening is designed to detect breast cancers earlier than would have occurred in the absence of screening so that treatment confers a benefit when applied sooner, whereas breast cancer prevention strategies can be used to avoid disease and its therapies. While lifestyle changes such as avoiding obesity or reducing alcohol intake could potentially reduce risk of developing breast cancer –69, hormAs noted earlier, the CISNET models can also be useful to assess the potential impact of hormonal therapy for primary prevention in women who have survived childhood cancer and are at high risk for developing a subsequent breast cancer. This includes female survivors who received chest radiation  and poteThe CISNET Consortium embodies the key features of team science. Team science is an emerging interdisciplinary field that empirically examines scientific collaboration described as the processes by which members of research teams organize, communicate, and conduct research in an interdependent fashion . TransdiSome challenges typically inherent in team science have been effectively managed in CISNET, including high disciplinary diversity of membership, knowledge integration, relatively large size , goal alignment, geographic dispersion, and high task interdependence. Factors contributing to effective management of common challenges have included strong commitment by team members to the mission of the consortium, collegial respect, and consistent support by champions at grant funding agencies. Furthermore, reports including multiple disease models demonstrate how scientific impact is increased by transdisciplinary collaborations that reach across single organ and disease types ,80,81.However, other areas present opportunities for greater emphasis in future endeavors. First, “permeable boundaries”  across mSecond, traditional academic laboratory structure, including the demands of tenure and promotion considerations, create challenges for the support of junior modelers and early career researchers. Programmers for each modeling team usually require over a year of training prior to gaining independence for modifying software code and executing modeling scenarios within the timeline constraints of multiple ongoing projects. To address these challenges, the CISNET Consortium has increasingly added training and mentoring opportunities for junior modelers to expand the pipeline and ensure the ongoing impact of CISNET after each grant cycle ends.Making the CISNET breast cancer control models accessible to the broader research community poses opportunities and challenges. Due to the complexity of the models and increasing use of the models by policy makers, it is important to increase the transparency of the models. To this end, we recently published a special issue of a journal dedicated to CISNET breast cancer models in which we described all main inputs and each individual model in great detail . The modWhile past collaborations with policy-making bodies such as the CDC or the USPSTF have been fruitful ,88,89, oIn conclusion, over 2 decades, the 6 modeling teams in the CISNET Breast Working Group and their consultants and collaborators have produced numerous examples of impactful systems modeling. Ongoing work is highly likely to provide new insight into avenues for reducing the burden of breast cancer. Collateral benefits of the CISNET Consortium have included advances in modeling and simulation techniques, support of the careers of junior scientists, and the provision of research tools for the broader scientific community. CISNET researchers are committed to increasing their focus on leveraging their powerful modeling resources to reduce the long-standing racial disparities in breast cancer outcomes, and we encourage other scientists to draw on team science approaches to also use modeling for this purpose.