Breast cancer (BC) risk assessment models base their estimations on differentaspects of a woman’s personal and familial history. The Gail and Tyrer–Cuzickmodels are the most commonly used, and BC risks assigned by them varyconsiderably especially concerning familial history. In this study, our aim wasto compare the Gail and Tyrer-Cuzick models after initial screening for familialhistory of cancer in primary care using the FHS-7 questionnaire. We compared 846unrelated women with at least one positive answer to any of the seven FHS-7questions (positive group) and 892 unrelated women that answered negatively(negative group). Concordance between BC risk estimates was compared byBland-Altman graphics. Mean BC risk estimates were higher using the Tyrer-CuzickModel in women from the positive group, while women from the negative group hadhigher BC risk estimates using the Gail model. With increasing estimates,discordance also increased, mainly in the FHS-7 positive group. Our results showthat in women with a familial history of cancer, the Gail model underestimatesrisk and the Tyrer-Cuzick seems to be more appropriate. FHS-7 can be a usefultool for the identification of women with higher breast cancer risks in theprimary care setting. A wide variety of empiric and mathematical risk assessment models based on personal andfamilial risk factors have been developed to estimate a woman’s risk of developingbreast cancer. Established risk assessment models to quantify breast cancer risk includethe Breast Cancer Risk Assessment Tool (BCRAT), Tyrer–Cuzick , Claus and Ford models, BOADICEA and BRCAPRO,among others. However, these models base their respective risk estimations on differentaspects of a woman’s personal and familial history and thus, are nopt equally wellcalibrated for all populations , and at inclusion in thestudy, all women answered the FHS-7 questionnaire about their family history of cancer(Two groups were included in the present study: (a) FHS-7 positive: 846 unrelated womenwith a positive family history of cancer (at least one positive answer to any of theseven questions), and (b) FHS-7 negative: 892 unrelated women that answered negativelyto all questions of the same instrument. The study was approved by the InstitutionalReview Boards of the participating institutions.BRCA1 or BRCA2 mutation, as well as theestimation of breast cancer lifetime risks, through the analysis of family history,hormonal and reproductive factors, and personal characteristics (The Gail breast cancer risk assessment tool (BCRAT) was constructed to estimate the riskof developing breast cancer in women undergoing annual mammographic screening. Briefly,it is an unconditional logistic regression model that provides a ratio of risk in womenwith specified risk factors compared with the risk in women without risk factors for thedisease. The model is able to estimate current (within 5 years) and lifetime (up to theage of 90 years) risk of breast cancer .Althoughttps://www.r-project.org/).Breast cancer risk estimates (lifetime and within 5 years for the Gail model and 10 yearsfor the Tyrer-Cuzick model), as well as demographic and clinical information wereobtained in patient interviews. Since the upper age limit used in the two models differs(80 years in the Tyrer-Cuzick Model and 90 years in the Gail model), we modified the agelimit of the Gail Model to age 80 years using the software BCPCARE kindly provided byProf. Mitchell Gail to facilitate comparisons. To compare the risk estimates obtainedwith both models, the Bland-Altman method  was usedLifetime breast cancer risk estimation using risk prediction models is an important stepin defining breast cancer screening plans and breast cancer risk reducing interventions.Different risk estimation models have been published, among which the most commonly usedare the Gail and Tyrer-Cuzick models. Previous studies have shown that these modelsperform differently in distinct scenarios and should be considered with caution for usein women at different risk levels (Underestimation of lifetime breast cancer risk has relevant implications for a woman,since classification in one of the higher risk groups prompts the health careprofessional to discuss and/or recommend specific cancer risk reducing interventionsthat would not be offered just to any individual. These interventions include screeningwith mammograms and breast MRI, chemoprevention, and risk reducing surgeries for womencarrying mutations in high penetrance cancer predisposition genes (For these reasons, breast cancer risk estimation is very important, and choosing a modelthat underestimates this risk may result in increased morbidity and mortality by breastcancer. At the same time, superestimation of risk may result in unnecessary screeningprocedures that will impose a burden to the patient and the health care system, which isespecially relevant when one considers the high prevalence of breast cancer in differentregions of the world and the increasing incidence rates observed in the past few yearsin several countries (BRCA1 or BRCA2 mutation-negativeversus subgroups typically classified as high-risk. The authors observed better overallcalibration and discrimination for the Tyrer-Cuzick than for the Gail model in thesehigh-risk groups (Previous studies showed that the Tyrer-Cuzick is more accurate than the Gail model incertain setttings (Therefore, several authors have shown that the Tyrer-Cuzick model performs accurately inthe identification of moderate to high risk of developing BC, and currently it is theinstrument that includes the largest number of established BC risk factors . The appIn conclusion, we show that, after an initial screening for family history of cancerusing a simple questionnaire, the Gail and Tyrer-Cuzick models show differentperformance profiles according to the presence or absence of specific high-risk familyhistory features. We observed that both the Gail and Tyrer-Cuzick models performsimilarly in individuals without a significant cancer family history, but in those withhigh-risk features from the family history of cancer, or from reproductive and/orhormonal risk factors, the Gail model underestimates risk. Potential limitations of thisstudy include the low specificity of FHS-7 and the absence of long-term follow-upregarding breast cancer diagnosis as an outcome in both study groups. However, ourproposal was to use FHS-7 only as an initial family-history screening tool in theprimary care setting, and thus qualify the choice of a breast cancer risk assessmentstrategy that is most appropriate for each clinical scenario. The assessment of familialrisk of breast cancer in a primary care setting is particularly relevant in populationswith high incidence rates of the disease.


Ultrasound (US) has traditionally been used as a mammography (MMG) support in diagnostic tool for detection of invasive breast cancer (BC), particularly in young women and with dense breast where the US sensitivity is very high (95.7%) -2. An adIn our previous work, four different risk categories were defined according with Modena criteria and Gail model calculation: BRCA carriers, high risk (HR) group with a life-time risk cut-off of 30-50%, intermediate risk (IR) group (life-time cut-off ranging between 18 and 29%) and slightly increased risk (SIR) group (less than 18%) [At the US diagnosis, we found mostly ductal infiltrating carcinoma compared with in situ carcinomas (DCIS) (82.7% vs. 17.3%), but the DCIS rate was significantly increased in the IR group. This finding justifies the evidence that patients at intermediate risk develop less aggressive tumors than patients at high risk or BRCA carriers. The US diagnosed tumor size was less than 2 cm in 86% of cases, demonstrating that US is able to detect BC at early stage in the most of cases. Once again, cancers diagnosed by US in BRCA carriers were mostly triple negative BC, with high grade. Of interest, the US sensitivity was a little bit decreased after 50 years (31.5% in patients aged ≤ 50 years vs. 27.7 % in patients older than 50 years), when breast density become more adipose [In our experience the real US benefit was represented by the six-monthly examination reaching a 29.4% of sensitivity, without significant differences amongst the risk groups and age range. The US sensitivity in BRCA carriers was 22.7%, but in that population breast MRI alone represented the most sensitive technique (94%). For patients belonging to the HR, our schedule and timing based on six-monthly US starting at 30 years, biennial mammogram from 30 to 36 years, annual from 36 years on, should be referred as screening protocol, since provide a total sensitivity of 100%. On the other hand, in the IR group, particularly for women aged more than 50 years, the US sensitivity was very low (18.2%).We think that in women at high risk without BRCA mutations is justified to enhance the screening by US, since it is a safe technique at low costs. This message is very important for women who are the most considerable part of the population at risk and for which standard protocol screening do not provide sufficient sensitivity in BC diagnosis. For women at IR, annual mammogram without ultrasound has probably sufficient sensitivity. Women with BRCA mutation have an advantage in BC diagnosis by MRI even if a mortality reduction have not been yet demonstrated.We suggest that breast US can be a useful addition to mammogram for BC screening, in certain cases. A correct identification of different BC risk levels is needed to provide several screening protocols, in which US could be added without significant additional costs and reducing the incidence of interval breast cancer with early diagnosis.Results from the ongoing HIBCRIT3 study comparing breast annual MRI plus US after 6 months vs. annual MRI plus MMG and US after 6 months in BRCA1/2 carriers, are awaited to provide further evidence, in order to improve the screening of women with mutations in hereditary breast cancer genes.


Notably, sixty percent of women with the lowest risk based on non-modifiable risk factors will never reach the risk level recommended for mammography screening. A combination of easily assessable breast cancer risk factors can help to identify women at high risk of developing breast cancer for targeted screening. A large number of high-risk women could benefit from risk-reduction and risk stratification strategies.Incidence of breast cancer is rising rapidly in Asia. Some breast cancer risk factors are modifiable. We examined the impact of known breast cancer risk factors, including body mass index (BMI), reproductive and hormonal risk factors, and breast density on the incidence of breast cancer, in Singapore. The study population was a population-based prospective trial of screening mammography - Singapore Breast Cancer Screening Project. Population attributable risk and absolute risks of breast cancer due to various risk factors were calculated. Among 28,130 women, 474 women (1.7%) developed breast cancer. The population attributable risk was highest for ethnicity (49.4%) and lowest for family history of breast cancer (3.8%). The proportion of breast cancers that is attributable to modifiable risk factor BMI was 16.2%. The proportion of breast cancers that is attributable to reproductive risk factors were low; 9.2% for age at menarche and 4.2% for number of live births. Up to 45.9% of all breast cancers could be avoided if all women had breast density <12% and BMI <25 kg/m In particular, breast cancer incidence in Asia is rising more rapidly than in the West2. An annual increase of 3.9% was reported in Singapore, compared to 1.5% in the United States3. Multiple risk factors for breast cancer are well-established; these include breast density, reproductive (parity and age at first birth), menstrual , and modifiable lifestyle factors such as body mass index (BMI), hormone replacement therapy (HRT) use and alcohol consumption6. Quantifying the proportion of breast cancer cases that can be attributed to each of these risk factors can aid in developing population-specific public health interventions and strategies to reduce breast cancer incidence.Breast cancer is the most common cancer among women, accounting for 25% of all female cancers globally7. The PAR of any risk factor is dependent on the magnitude of the association between the risk factor and the disease, and the prevalence of the risk factor in the population9. The prevalence of risk factors may vary among different populations and can be influenced by lifestyle preferences, cultural habits, environmental factors and socio-economic status. PAR estimates may thus vary across different populations and at different time periods even if the association between the risk factor and disease does not change9.The population attributable risk (PAR) is the proportion of disease cases that would not have occurred if the risk factor was removed from the population12, Europe15 and Australia16. Reported PARs of modifiable lifestyle risk factors ranged from 1% for alcohol consumption (relative risk of 1.4 was used)10 to 39.3% for breast density (relative risks of 1.57 and 1.84 were used) assessed using the Breast Imaging Reporting and Data System (BI-RADS)8. In addition, PAR of the same risk factor can have a wide range depending on the population studied (e.g. 2%10 to 19.4%13 for HRT use).Previous studies on PAR of breast cancer risk factors have mostly been conducted in Western populations in the United States17. The region is characterized by the coexistence of various cultures and ethnic groups. Thus far, the PAR of breast cancer attributed to breast cancer risk factors have been studied mainly in East Asian populations (e.g. Korea18 and mainland China20). Singapore is a multiracial and multicultural country comprising of three main Asian populations, with ethnic Chinese (74.3% of the citizen population), Malays (13.4%), and Indians (9.1%) making up the majority of the population21. In this study, we examined the impact of known breast cancer risk factors, including BMI, reproductive and hormonal risk factors, and breast density on the incidence of breast cancer.Southeast Asia is a geographically expansive and populous region amounting to ~656 million people in 2018, equivalent to ~8.6% of the world’s population22. Further details of this program have been previously described22. In brief, women were excluded if they had cancers of the breast or other sites (except non-melanoma skin cancer), had mammography done or breast biopsy in the past one year prior to screening, or were pregnant . Further exclusions were made due to death (n = 468) or invalid address (n = 167). Of the eligible 67,656 women, 41.7%  participated and were screened as part of SBCSP  of participants; which included 14 (13.9%) participants with reported age <50 years and 87 (86.1%) participants with missing details on intended study variables. Ethical approval and the waiver of the need for informed consent was approved by SingHealth Centralised Institutional Review Board (REF: 205-001). In addition, this study used existing anonymous data. All research was performed in accordance with relevant regulations.The Singapore Breast Cancer Screening Project (SBCSP) was a population-based prospective trial of screening mammography in Singapore. In this project, 69,473 women aged 50–64 years were randomly selected and invited for a single two-view mammogram examination from 1994 through 199723.Incident cases of invasive and non-invasive breast cancer cases diagnosed after study entry (date of screen) until December 2007 (the end of study), were identified through record linkage with the Singapore Cancer Registry using unique national registration identity (card) numbers. The population-based national registry records all cases of cancer diagnosed in Singapore, with an overall completeness of 98.1%2: <18.5, 18.5–24.9, 25.0–29.9, 30.0–34.9, ≥35.024) and ever smoke . Non-modifiable risk factors studied included age at study entry , ethnicity , family history of breast cancer , and personal history of benign breast disease . Reproductive and hormonal characteristics included menopausal status , age at menarche , number of live births , age at first live birth , breastfeeding history , ever used contraceptive  and ever used hormone replacement therapy . Breast density in the form of percent mammographic density was calculated from screen-film mammography images originally collected during the SBCSP which had been digitalized between February 2012 and February 2013. Breast density was categorized into quartiles, with values of 12.29%, 18.24% and 26.03% corresponding to the first quartile, median and third quartile respectively. A fully-automated thresholding method was used to measure breast density as described previously26.The participants of SBCSP completed a questionnaire, prior to receiving their mammogram screening results, detailing their demographics and lifestyle factors on the day they attended screening, which included modifiable risk factors, BMI  compared to the reference level.The PAR is useful in estimating how much of the disease burden in the population could be reduced if certain risk factors were eliminatedThe relative risk  and family history (reference level = no) would have women with BMI of 18.5–24.9 and without family history as the reference category. Similar to the analysis of one risk factor, PAR was estimated by shifting only women in higher risk category to the reference category.29. Projection of absolute risk distribution was based on the breast cancer incidence rates30 and mortality rates in Singapore31. A random selection of 50% of study participants was used to build the logistic model to categorize women with differing risks of breast cancer  based on non-modifiable risk factors . The relative and absolute risks of developing breast cancer by percentiles of non-modifiable risk factors distribution were calculated using the remaining 50% of study participants. Bootstrapping using 2,000 iterations was done to estimate the odds ratios used in the projection of absolute risks. Absolute risk was computed only for non-modifiable risk factors as the values do not change over a woman’s lifetime. For comparison, we added BMI, smoking status and breast density to the risk model and estimated the absolute risk based on the new risk categorization of women. The discriminatory accuracy of non-modifiable risk factors on predicting breast cancer risk was evaluated using the area under the receiving operating curve (AUC), estimated using the pROC package in R32. The median and interquartile range of AUC from the bootstrapping method was reported. All statistical analyses were performed using R (version 3.4.3).The age-specific absolute risk of developing breast cancer in each risk categories was calculated under the assumption that the average age-specific breast cancer incidence over all risk categories agreed with the population breast cancer incidence. The details of the method have been previously describedThis study uses existing anonymous data from the Singapore Breast Cancer Screening Project. The original study received ethics approval from SingHealth Centralised Institutional Review Board (REF: 205-001).All authors approved the manuscript and consented to its publication.A total of 28,130 women were included in this study, of whom 474 (1.7%) developed breast cancer. The study population was predominantly ethnic Chinese (84.2%) with smaller percentages of Malays (5.6%), ethnic Indians (5.0%) and other races (5.2%) , and age at menarche ≤13 (35.1%). Other risk factors were of low prevalence; among all women 7.2% were nulliparous, 5.3% had a personal history of benign breast disease and 2.6% had a family history of breast cancer.Among risk factors significantly associated with breast cancer, the most prevalent risk factor was ethnicity, with 94.4% non-Malays, followed by high breast density (65.2%), BMI ≥25 kg/m2 (Table Up to 16.2% (95% CI: 14.5–17.4) of breast cancers could be prevented if all women had a BMI of <25 kg/m2 Table .Table 3PMalays (reference level) were found to have the lowest risk of breast cancer, followed by Indians  and Chinese   of all breast cancers can potentially be prevented if all women had breast density ≤12.29% (Table th percentile) as categorized by non-modifiable risk factors were 1.98 (95% CI: 1.73–2.27) times more likely to develop breast cancer than women with average risks (30–60th percentile)  to the lowest risk category (30th percentile and below), more than two times as many breast cancer cases were found. Women in the bottom 60% of the risk distribution will never reach the risk threshold for screening, while women in the highest risk group will reach the risk level recommended for mammography screening when they are ~41 years old . Women with the highest risk of breast cancer (>90e) Table . The absold Fig. .Table 4PThe inclusion of modifiable risk factors (BMI and smoke) and breast density resulted in slight improvement in the median discriminatory accuracy (AUC [interquartile range]: 65.6 [64.5–66.8]). The risk model was improved in its ability to differentiate women in the 60–100 percentile from women in the 0–60 percentile  were individually associated with breast cancer PAR of no more than 10%. Sixty percent of the women with breast cancer risk categorized by non-modifiable risk factors will never reach the risk threshold recommended for mammography screening.The modifiable risk factor, BMI, has the potential to reduce up to 16.2% of all breast cancer cases in Singapore if all women with high BMI in the population were to attain a BMI of <25 kg/m13 to 22.8%8 observed in post-menopausal Western women. Excess adipose tissue and increased aromatase activity in obese post-menopausal women may increase their levels of circulating endogenous estrogen, which in turn increases breast cancer risk42. This finding has potential repercussions for public health as BMI is one of the risk factors that are easily measurable and modifiable.The PAR (17.7%) of BMI in our study falls in the range of 2.4%44. Possible reasons reported include the tendency of Malay women to have more children, have their first child at a younger age and breastfeed over longer periods44. However, after adjusting for these factors, PAR of ethnicity for breast cancer remained large, 49.3% in all women and 52.4% in post-menopausal women. Additional socio-cultural factors, such as dietary preferences influenced by their religious beliefs may also play a role43.Concordant with previous studies, we found that Malay women were less likely to develop breast cancerUnlike other risk factors, the high PAR of ethnicity is mainly due to the much larger proportion of Chinese than Malays . Similarly, the risk associated with breast cancer is high in women with a family history of breast cancer (OR = 2.31). However, few women reported a family history of breast cancer, resulting in fewer potentially avertable breast cancers when family history is considered a risk factor.et al.9 and Barnes et al.13, who reported PAR of 8.6% and 7.7% respectively. Sprague et al. reported a higher PAR estimate of 18.8%, using women who were at least 15 years at menarche as the reference group11. The association between early menarche and increased breast cancer risk can be attributed to the earlier exposure and higher levels of estrogen experienced by women who had early menarche45. Similar to the findings by Li et al.20 and Park et al.18 PARs for reproductive factors was small.Less than 10% of breast cancers in Singapore were attributable to the young age at menarche (age ≤13) after we accounted for other factors like breast density and ethnicity. This is consistent with findings by Tamimi 8. The association between breast density and breast cancer is well-established and confirmed by many studies since it was first described by Wolfe in 197647. Women with dense breasts were reported to have a four to six times greater risk of breast cancer compared to those without any visible density, with 26–28% of all breast cancer cases being attributable to having breast densities greater than 50%49. While reducing breast density holds higher potential in decreasing the risk of breast cancer as compared to lowering BMI, the use of anti-estrogen drugs such as Tamoxifen to reduce breast density is not common practice36. Obesity is related to a multitude of other diseases such as type 2 diabetes and coronary heart disease50. In view of a high prevalence of sedentary behavior and general lack of physical activity, much can be achieved to improve overall health and decrease breast cancer incidence by lowering BMI51.Our study in is agreement with results from Western populations that, among the well-known risk factors of breast cancer, breast cancer is highly attributable to high breast density17. However, only 66% of the main target group of women aged 50 to 69 ever had a mammogram, and half of them do not come back for regular screening at two-year intervals, negating the benefit of mammography . Conveying how easily assessable non-modifiable risk factors can affect the risk of breast cancer may persuade high risk women to go for regular screening.Risk factors such as ethnicity, family history of breast cancer, personal history of benign breast disease, age at menarche, and number live births are by nature non-modifiable. Information on non-modifiable risk factors can help reduce breast cancer incidence by means of stratifying the population at risk of developing breast cancer for targeted screening. The current nationwide screening strategy,which started in year 2002, recommends that women aged 40–49 years go for routine mammography screening every year, and women aged 50 years and above, every two years21). Participation in opportunistic screening during the study period between 1992–1994 was low as mammography screening was expensive52. Women attending screening may also be more health conscious, which can potentially underestimate PAR. The sample size was also limited; thus, we could only examine broader absolute risk categories. In the consideration of breast density as a modifiable risk factor, the drugs used to induce breast density reduction may also reduce breast cancer risk through other mechanisms. In this case, the PAR associated with breast density may be overestimated34. In addition, the associations between some risk factors and breast cancer may differ according to the tumor subtype53. For example, Millikan et al.53 found that basal-like cases exhibited opposite associations to those observed for luminal A for risk factors including parity, age at first pregnancy and breastfeeding. Further stratification by tumor subtype may more clearly reveal the relative importance of risk factors for each subtype.We acknowledge that our study has some limitations. Our screening population only included women who were at least 50 years of age, of whom ~90% were post-menopausal. As such, our results may not be generalizable to pre-menopausal women and women of younger age. Due to the small numbers of Malay and Indian participants, we were not able to estimate PAR for each risk factors by ethnicity. However, the impact of the lack of ethnicity specific estimates may not be large in Singapore  to reduce their risk.A substantial proportion of breast cancers (16.2%) in SBCSP can be attributed to overweight/obesity. In addition, up to 45.9% of breast cancer could potentially be avoided if all women had low BMI and breast density. Women with breast cancer risk below the 60Supplementary Information.


Breast cancer is the leading cause of death among reproductive-age women worldwide and the second leading cause of death among women in Ethiopia. Regular breast self-examination is the most cost-effective methods for early detection of breast cancer. Despite this fact, breast self-examination was low among women in the general population and it was not well documented among health care workers. Therefore, this study intended to assess the magnitude of breast self-examination practice and associated factors among female healthcare workers in public health facility in West Shoa Zone, Ethiopia. Institutional based cross-sectional study was conducted among 379 female healthcare workers. The study participants were recruited by lottery method. Data were collected from March to April 2019. Data were entered into Epi data version 4.5 and analyzed using SPSS version 25. Bivariate and multiple logistic regressions analysis were done. With 95% CI, the level of significance was decided at P-value ≤ 0.05.The magnitude of regular breast self-examination practice was 32.6%. Educational levels, breast cancer awareness, knowledge towards breast self-examination were predictors of breast self-examination. Regular awareness creation forum on breast self-examination technique, risk factors, and related matters should be facilitated so that all female health care workers will be reminded. Breast cancer is the leading cause of cancer mortality worldwide. In 2017, an estimated 252,710 new cases of invasive breast cancer were diagnosed among women and approximately 40,610 women were expected to die from breast cancer [Since breast cancer is serious, an important factor in the prognosis of breast cancer is early detection of the disease . Breast Early detection of breast cancer plays an important role in decreasing its morbidity and mortality. Studies from some African Countries indicated that the duration of onset of breast cancer to care seeking period is more than 1 year and implies that improving timely detection of breast cancer among women in SSA could have a great impact on stage at diagnosis, and gives opportunity for improved prognoses and treatment options. Most of the early breast tumors are self-discovered and the majority of early detection is by BSE, and 80% may be detected by expert professionals . EvidencEarly detection and diagnosis rate of breast cancer among Ethiopia women is considerably low when compared to women in Western countries. This fact put the Ethiopian women’s to be diagnosed at the late stage of the disease. Therefore, many women miss early detection and treatment opportunities due to the lack of information and knowledge of breast cancer, as well as cancer screening skills.The magnitude of BSE varies among different segments of females. For instance, a study conducted among undergraduate university student in Addis Ababa indicated 21%, study in Debra Birhan indicated 28%, while the study among female health care workers indicated somehow greater prevalence. For example study among health extension workers in Gojam, Northern Ethiopia shows 37% and 32% in Debre tabor. Even though the finding among health care workers seems relatively higher when compared to other groups of females, it has two main limitations. The first is different studies used different tools for assessing the BSE practice and this curbs comparison of the studies. Secondly, even with the currently existing evidence, the difference in the prevalence of BSE among female health care workers and other female groups is not as it should be , 12.Therefore, this study attempted to assess the BSE practice and associated factors using input from different works of literature and applying the standard and correct definition of BSE.This study was conducted in seven Public hospitals found in west Shoa Zone Oromia regional state, Ethiopia using institutional-based cross-sectional study design. Data were collected from March to April 2019.The sample size was determined using single population proportion formula with the assumption of marginal error of 5%, 5% of non-response rate, 95% confidence level and the prevalence of the breast self-examination practice to be 37% from the study conducted in East Gojjam, North Ethiopia .Since the sample was drawn from a finite population, the correction formula was applied. Finally, the sample size of 379 was determined. The calculated sample size was proportionally allocated to each Hospital based on the number of female health care workers in the hospital. After proportionally assigning sample size to each hospital, a simple random sampling technique was applied to select study participants.All-female healthcare workers who were actively on job during data collection at each selected hospitals were included.Data was collected using a self- administered questionnaire. The questionnaire was developed in the English language after reviewing and extracting from different pieces of literature developed for the same purpose. For measuring knowledge towards BSE, there were 10 questions developed. Answering a correct answer will result in scoring a mark and loosing will attract zero scores. Accordingly, the final total mark will be added up out of ten and graded for the decision of knowledge level.To measure attitude towards BSE, Likert scale based items were prepared . The scales reached from strongly agree to strongly disagree. To assess the internal consistency of the items, Cronbach alpha was assessed and it was 0.87 indicating good internal consistency of the items.Good practice of breast self-examination: Those who performed breast self-examination practice a week after each menses by their palm and middle three fingers otherwise called poor practice.Good knowledge: Participant those who answered greater than 75% of the 10 knowledge questions towards breast self-examination.Average knowledge: Participants who answered 50–75% of knowledge questions toward breast self-examination.Poor knowledge: Participants who answered less than 50% of knowledge questions toward breast self-examination.Favorable attitudes: Participants who scored points equal to or greater than mean score of breast self-examination related attitude questions as measured by Likert scale.Unfavorable attitude: Participants who scored points less than the mean score of attitude questions , 14.The collected data were checked visually for completeness, then coded and entered into Epi data version 4.5 statistical packages. Descriptive analysis was computed. To assess the association between dependent and independent variables by controlling for confounders, first binary logistic regression was run and variables with P-value ≤0.25 and the variables which are known to have an association with dependent variables from reviewed literature were selected for Multiple logistic regression analysis. Statistical significance was declared at P-value < 0.05 with 95% confidence interval (CI).The questionnaire was developed by reviewing relevant pieces of literature on the subject.to ensure reliability.The questionnaire was pre-tested and modified where necessary.One day training was given for data collectors and supervisors.To ensure the data quality of our study the following measures were taken:Breast self-examination practice (BSE).Ethical clearance was obtained from the Ethical Review Committee of the College of Medicine and Health Sciences, Debra Markos University. During the fieldwork, the objective of the study was clearly explained for the study participants, the confidentiality of the data to be collected and the right not to participate was also assured. Before starting the data collection process, written consent was taken from each respondent after they read and signed the consent form.A total of 340 female healthcare workers were responded to the distributed questionnaires, making a response rate of 89.7%. The mean age of the respondents was 28.0 ± 4.8 SD which was ranging from 19 to 43. About 187 (55.0%) was married. Out of the total participants, about 198 (58.2%) were nurse professionals and more than two thirds (232) of the participants were degree holder followed by diploma 71 (20.9%) (Table The majority of 227 (66.8%) the study participants know the screening methods of breast cancers. The commonly mentioned breast self-screening techniques were: mammography (5.0%), clinical breast self-examination (7.9%), and 23.2% mentioned breast self-examination. About 106 (31.2%) stated the three screening methods of breast cancer. Out of the total participants, 228 (67.1%) ever heard about breast self-examination. The main source of information of the breast self-examination was the knowledge they received during the class lectures in the University or College which accounts for 132 (38.8%)  had a favorable attitude towards breast self-examination.In bi-variate logistic regression three variables i.e. level of education, work experience, and knowledge of BSE have association with breast self examination, while in multiple logistic regression, level of education, attitude towards BSE, and knowledge of BSE were independent predictors of breast self examination practice Table .Table 2MIn this study, the prevalence of the correct and regular practice of breast self-examination was low (32.6%). This finding is comparable with study conducted among midwifes and Nurses in Turkey and lower than finding from Eritrea (60.6%) , 16. ButThis study indicated, as the educational level of the females’ increase, the likely hood of BSE practices also increase. Compared to those females’ in diploma level, degree holders were 12 times more likely to practice BSE correctly and regularly. This finding is consistent with a study conducted in Western Ethiopia Female health care workers who don’t teach BSE to their clients during routine clinical activities were more than 50% less likely to practice breast self-examination 0.42   compared their counterparts.This study also showed that there is a significant association between participants’ level of knowledge about breast cancer and BSE practice. The commonest reasons reported why the females were practicing BSE were: for early detection and seeking treatment and fear of developing breast cancer in the future.Though they had sufficient knowledge and awareness about BSE, about 27% of the females didn’t practice it because of negligence. This finding is similar with finding from Asmara which was conducted among nurses .In conclusion, the finding of the study indicated that BSE practice was low and it was associated with different factors. There was also a gap in knowledge and awareness among the female health care professionals towards BSE, and even those who had sufficient knowledge were not practicing BSE because of negligence.Short term training on breast cancer and breast self-examination should be better organized by the hospital administrator.The self-reported information is subjected to bias specifically to social desirability bias. There was no internationally recognized standardized tool to assess BSE. Besides, since the majority of the existing works of literature were conducted among no health care females, this results in a variation of measurements which limit us comparing the findings of this study with other studies. Also, we didn’t include a qualitative aspect in this study which could have strengthened the finding of a quantitative one.


Indeed,CVD is a leading cause of mortality in breast cancer survivors.5The mechanisms by which these patients are at increased cardiovascular risk are multipleones, including the effects of cancer itself  or due to the side effects related tochemotherapy and radiotherapy .6 Some of these pathways may haveprognostic significance: a previous investigation showed that autonomic modulation inbreast cancer patients is an independent predictor of cardiovascular risk.6Breast cancer is the most frequent malignancy in women worldwide, and the second leadingcause of cancer mortality.Arquivos Brasileiros de Cardiologia, the study"Changes in Cardiac Autonomic Modulation in Women with Breast Cancer Using AromataseInhibitors and the Relation with Biochemical Variables"7 was aimed to explore not breast cancer but theassociations of one of its treatments  with markers ofautonomic dysfunction, as well as metabolic and inflammatory parameters inpostmenopausal women. The rationale for exploring this specific therapy was based on arecent meta-analysis showing that prolonged AI use had a marginally effect of having aCVD event  as compared to placebo.8In this issue of the 7 theauthors performed a cross-sectional analysis comparing two groups of participants: 1)women with breast cancer, treated with AIs and 2) postmenopausal women without breastcancer. For the evaluation of the autonomic modulation, heart rate was recordedbeat-to-beat for 30 minutes and the series of RR intervals obtained were used tocalculate rate variability indices (RVI): mean RR ms, SDNN  ms, mean Heart Rate (HR), RMSSD  ms,NN50 (number of pairs of successive NNs that differ by more than 50 ms) count, p NN 50%, RRtri (RR triangular), TINN ms, SD1ms, SD2 ms, LF (low frequency)ms2, HF(high frequency) ms2. LF; HF ms2. Despitesome criticism, all these parameters provide an indirect evaluation of autonomicfunction. In addition, the following metabolic and inflammatory parameters wereanalyzed: fasting glycemia, triglycerides. HDL-cholesterol and C-reactive protein (CRP).The study showed that lower values of HR variability indices were observed in breastcancer patients in relation to the control group. Besides, there was an inversecorrelation between the indices SDNN, SD2 and HFms with triglycerides. No statisticallysignificant correlations were found between HR variability indices and other biochemicalvariables.In the current investigation,While this study is timely, addressing the increasing awareness of the cardiovasculareffects attributed to cancer and its treatments, there are significant limitations thatdeserve an appropriate discussion. This is a small cross-sectional study addressingautonomic and cardiovascular parameters in patients already on AI treatment. The lack ofbaseline measurements (before starting AI therapy) prevent any conclusion from beingdrawn on whether the main results were related to AIs, the breast cancer per se or someother residual factor. Regarding the latter, the authors did not evaluate the potentialrole of important confounding factors (such as hypertension or chronic use ofmedications) that could affect autonomic modulation or the cardiovascularbiomarkers.9 Based on theincreasing prevalence of breast cancer worldwide and the related burden of survivors, itis crucial to clarify the cardiovascular safety of the related drugs. The benefits ofcancer treatment should be balanced with the presence and magnitude of severe sideeffects - including cardiovascular events - in the long-term follow-up.Having said that, this study raised more questions than answers, but certainly stimulatesadditional investigations testing the cardiovascular safety of this importantchemotherapy class. In the past decade, AIs have been the recommended first-lineadjuvant endocrine therapy in postmenopausal women with hormone receptor-positive breastcancer; they are associated with improved disease-free survival and overallsurvival.


Published genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aimed to identify an efficient polygenic predictor for BC based on best available evidence and to assess its potential for personalized risk prediction and screening strategies.Four different genetic risk scores  and their combinations (metaGRS) were compared in the subsets of two population-based biobank cohorts: the UK Biobank  and Estonian Biobank . In addition, correlations between different genetic risk scores and their associations with BC risk factors were studied in both cohorts.2 - based on 75 and 898 Single Nucleotide Polymorphisms, respectively) had the strongest association with prevalent BC status in both cohorts. One standard deviation difference in the metaGRS2 corresponded to an Odds Ratio = 1.6  in the UK Biobank and accounting for family history marginally attenuated the effect . In the EstBB cohort, the hazard ratio of incident BC for the women in the top 5% of the metaGRS2 compared to women in the lowest 50% was 4.2 . The different GRSs were only moderately correlated with each other and were associated with different known predictors of BC. The classification of genetic risk for the same individual varied considerably depending on the chosen GRS.The metaGRS that combines two genetic risk scores  contains supplementary material, which is available to authorized users. Breast cancer (BC) is the most frequent cancer among women in the world, being also the second leading cause of cancer death in women in more developed regions after lung cancer . As earlIt has been estimated in twin studies that the heritability of breast cancer ranges from 20 to 30% . HoweverBased on the GWAS results, several efficient polygenic risk scores (GRS) have been developed for common complex diseases that in many cases could be used to improve the existing risk prediction algorithms –11. It iet al [et al [So far, several studies have combined the SNPs with established genome-wide significance in a GRS for BC. Sieh et al  used 86 l [et al  77 SNPs l [et al , 15. Altet al [In 2017, the large scale GWAS by Michailidou In the present analysis, the data of 32,557 female participants of the Estonian Biobank (EstBB)  has beenWe have also analyzed the data of 46,984 women (incl 3157 BC cases) of European ancestry from the UK Biobank  who passMore details about cohorts can be found in the Additional file k independent SNPs:βj is the weight of each SNP and Xij represents the number of risk alleles for j − th SNP  for the i − th individual, . Typically the estimated (logistic) regression coefficients from a large-scale GWAS meta-analysis are used as weights βj.The general definition of a GRS was based on the assumption that the polygenic component of the trait (e.g. disease risk) can be approximated by a linear combination of multigenic, if the number of SNPs (k) is relatively small, containing only the SNPs with established genome-wide significance from a GWAS. A polygenic GRS contained a large number of SNPs (often k > 1000) and was either based on all available independent SNPs (with pairwise correlation not exceeding a pre-defined threshold) or the ones that satisfy some p-value threshold (often ≥0.05).Published versions of GRS can be divided to two main categories. We called a GRS In the present paper, we computed two multigenic and two polygenic GRSs, whereas the polygenic GRSs were developed using the PRSice software .GRS70, based on Sieh et al [The score denoted by eh et al . The corresponding effect estimates of the filtered subset were then used as weights to compose the GRSs. Altogether, we used 22 different p-value thresholds to compose 44 different versions of GRSs – 22 based on first meta-analysis and 22 based on the second one. To select the best predicting GRSs out of 44, age-adjusted logistic regression model comparing 317 prevalent BC cases and 2000 randomly chosen controls in the EstBB cohort was used and the scores with the smallest p-value for the GRS-phenotype association were selected . The ren [et al , before 5.70, GRS75, GRSONCO, GRSUK) were calculated. Then GRSs were combined into three different versions of metaGRS, following the ideas by Inouye et al [metaGRS4 as the weighted average of all four GRSs, metaGRS3 as the weighted average of three GRSs with the strongest association with incident BC and finally metaGRS2 based on top two predicting GRSs. To construct metaGRS, log (odds ratios) of GRSs from training set from logistic regression model were used as weights.Thereafter, Pearson coefficients of correlation between all GRSs  the SNPs with available summary statistics were genotyped or imputed with acceptable quality in the EstBB; b) the pairwise correlations between SNPs did not exceed a pre-specified threshold of rFinally, the UK biobank data was used to further compare previously mentioned 7 GRSs and to address the attenuation of GRS’ effect while accounting for family history of BC and to study associations between BC risk factors and GRSs. While modelling in UK biobank, age at recruitment and 15 principal components were included in the model. The entire workflow was visualized in the Fig. All 7 GRSs were evaluated in the analysis of incident BC in 30,240 women from the EstBB cohort who did not have an existing BC diagnosis at recruitment and were not included in the case-control set used to select the best polygenic GRSs. Cox proportional hazard models were used to estimate the crude and adjusted Hazard Ratios (HR) corresponding to one standard deviation (SD) of the GRS. To assess the incremental value of GRSs when added to other known risk factors, the models were additionally adjusted for the absolute risk estimates from the NCI Breast Cancer assessment tool , based oFinally, associations between GRSs and variables related to female’s reproductive health and BC risk factors were explored using linear, logistic or Cox regression models depending on the type of dependent variable in both EstBB and UKBB cohorts  estimates per one SD of the GRS being 1.27  and 1.38 , respectively. Of all polygenic GRSs, the strongest association was observed for GRSONCO with p-value threshold p < 5* 10− 4 for SNP inclusion (898 SNPs). This resulted in OR = 1.44  per one SD of the GRS. The best version of GRSUK included 137 SNPs that satisfied inclusion threshold p < 5*10− 5 and resulted in OR = 1.34 . Similar effect sizes for all four GRSs were observed in the UKBB cohort  when also metaGRS2 was added to the model.Out of four studied GRSs, GRSBC Table  in the E2 distribution had 3.40 (95% CI 2.36 to 4.89) times higher hazard of developing BC than women in the lowest quartile. When the top quartile is further split into smaller percentiles  of 4.79 (95% CI 3.02 to 7.58) for incident BC compared to women in the lowest quartile, whereas HR = 4.20 (95% CI 2.84 to 6.23) for women in the top 5% compared to all women with metaGRS2 below the median. When the highest 5% percentile was compared with the rest of the cohort (women below the 95th percentile of metaGRS2), about three times higher hazard  was found. Compared to the women with metaGRS2 close to the median (belonging to the 40th to 60th percentile), the hazard of women in the top 5% of metaGRS2 was 2.7 (95% CI 1.77 to 4.18) times higher and the hazard of those with metaGRS2 below 40th percentile was almost 2 times lower  to develop BC.Women in the highest quartile of metaGRS2, 8.3% (95% CI 5.6 to 11.0%) for those between 85 and 95% percentiles and 7.4% (95% CI 4.85 to 10.0%) for the women in 75–85% percentiles. Cumulative BC incidence in the third, second and first quartile of the metaGRS2 distribution was estimated to be 5.8% (95% CI 4.4 to 7.3%), 3.6% (95% CI 2.4 to 4.8%) and 2.4% (95% CI 1.4 to 3.3%), respectively. No significant difference in BC hazard was seen between the two lowest quartiles (p = 0.26), with both of them having considerably lower incidence level than the cohort average .As seen from Fig. UK, GRSONCO or GRS75), 87.7% (28547) of women were assigned to non-high category with all three scores. However, 12.4% (4010) of women belonged to high category with at least one GRS. 0.33% (109) of women belonged to top 5% with all three scores compared to ~ 10% (3240) of the women, who belonged into high category only with one score  while adjusting for the metaGRS2. Known BC risk factors were only weakly associated with GRSs in both UKBB and EstBB cohorts  have been diagnosed with BC by the age of 70, women in the highest five percentiles of the metaGRS2 distribution reach the same cumulative risk level  by the age of 49, thus more than 20 years earlier. It is also notable that women with metaGRS2 level below median reach such risk level  only by age of 79, thus almost 10 years later. These findings suggest that the polygenic risk estimate based on metaGRS2 could be an efficient tool for risk stratification in clinical practice, for targeted screening and prevention purposes.We demonstrated that a metaGRS that combines a multigenic and a polygenic GRS for breast cancer - metaGRSGiven that the potential benefits of non-selective BC screening within certain age categories  have been under serious discussion in the medical community , persona2 with full pedigree-based family history data.However, while incorporating a GRS in clinical BC prediction, one should keep in mind that a GRS represents a mixture of different pathways, but is still not likely to capture the heritable component completely. As our findings indicated that a GRS and family history have independent predictive effects on BC risk, accounting for individual’s genetic information and family history (indicating either the mother has suffered from breast cancer or not or the status is unknown) simultaneously seemed to result in the better risk estimation than using only one of these predictors alone. However, more research is needed to assess the usefulness of combining our proposed metaGRS75 and GRS70) were highly correlated and if we could have included all original 86 SNPs instead of 70, correlation between GRS86 and GRS75 would have likely remained similar or decreased a little, as excluded SNPs from the original 86 SNPs were rather rare.As depending on a GWAS that is used as a basis, different (and not necessarily highly correlated) GRSs can be produced, it can be expected that those GRSs might emphasize the effects of different biological pathways. This hypothesis seems plausible in the light of several associations found between different GRSs and BC risk factors. Expectedly, GRSs including only a small number of significant SNPs  variants, or if deemed useful in the future, a combination of mendelian variants and GRS levels, but it definitely needs further studies.In summary, our results showed that an efficient polygenic risk estimate enables to identify strata with more than four-fold differences in BC incidence. This definitely calls for the development of personalized screening and prevention strategies that incorporate the GRS information, having the potential to considerably increase the benefits of nation-wide screening programs and reduce the existing controversies on their efficacy. However, one should be aware of the fact that a GRS is still a proxy of a true genetic risk and it is not uniquely defined – as more research accumulates, more efficient polygenic predictors could be developed that may re-categorize some previously stratified individuals into high or low risk groups. In addition, a GRS should ideally be combined with information on other genetic and non-genetic risk factors for best possible accuracy in risk assessment.Additional file 1:Table S1. Cohort characteristics of UK Biobank and Estonian Biobank. Table S2. Associations of breast cancer and standardized GRSs in the UK Biobank (with and without adjustment of family history) and in Estonian Biobank without family history. Table S3. Associations between GRSs and risk factors of breast cancer in Estonian Biobank. Table S4. Associations between GRSs and risk factors of breast cancer in UK Biobank. (XLSX 30 kb)Additional file 2:Figure S1. Associations of GRSs with prevalent breast cancer in EstBB data. Figure S2. Correlations between different genetic risk scores (GRSs). Figure S3. Power to detect an association between GRS and breast cancer status given the sample size of the case-control and prevalence of the disease. (DOCX 128 kb)


Purpose: Hispanic women are less likely to be screened for breast cancer than non-Hispanic women, which contributes to the disproportionate prevalence of advanced-stage breast cancer in this population group. Patient navigation may be a promising approach to help women overcome the complexity of accessing multiple health care services related to breast cancer screening and treatment. The goal of this study is to assess patient perception and cost-effectiveness of a multilevel, community-based patient navigation program to improve breast cancer screening among Hispanic women in South Texas.Methods: We used mixed methods—including focus groups of program participants and a microsimulation model of breast cancer—to evaluate the effectiveness and cost-effectiveness of the program on the target population. Program data from 2013 to 2016 were collected and used to conduct the analyses.Results: Focus groups showed that the patient navigation program improved patient knowledge, attitudes, and behaviors regarding breast health and increased the mammography screening rate from 60% to 80%. Cost-effectiveness analysis showed that the program could increase life expectancy by 0.71 years and yield an incremental cost-effectiveness ratio of $3120 per quality-adjusted life year compared to no intervention.Conclusion: The 3-year multilevel, community-based patient navigation program effectively increased mammography screening uptake and adherence and improved knowledge and behaviors on breast health among program participants. Future research is needed to translate and disseminate the program to other socioeconomic and demographic groups to test its robustness and design. Patient navigation has been shown to be effective in alleviating anxiety associated with the screening process and, thus, improving screening uptake and adherence.14 Recent research also has shown that patient navigation can be strengthened by integrating principles of behavioral economics—such as accurately estimating risk, personalizing information, invoking social norms, and providing incentives—to improve cancer screening in underserved populations.16Patient navigation may be a promising approach to help women overcome the complexity of accessing multiple health care services related to breast cancer screening and treatment.This study assesses patient perception and cost-effectiveness of a patient navigation program implemented in San Antonio, Texas. The program used patient navigators to provide comprehensive breast cancer screening services targeting underserved Hispanic women. Although the program has demonstrated an initial success by increasing the breast cancer screening rate among the target population, it is unknown how patients perceived the program and whether the program can be sustained in the long term with a favorable cost-effectiveness ratio.To answer these questions, we developed an innovative mixed method approach that combined qualitative focus groups with economic microsimulation. Focus groups would allow us to assess the impact of the program on patient knowledge, attitudes, and behaviors regarding breast health, while a microsimulation model of breast cancer progression would help us determine the long-term health impact and cost-effectiveness of the program. This innovative study design provides a comprehensive evaluation of both the effectiveness and long-term cost-effectiveness of the program, which would be more helpful to practitioners and decision-makers who are interested in implementing a similar program.A Su Salud Breast Health Program to increase the breast cancer screening rate of Hispanic women. The program primarily targeted Hispanic women 40 years of age and older enrolled in CareLink  who have never been screened for breast cancer or have not been screened in the last 5 years. The program included a health promotion media campaign, educational outreach, a patient navigation initiative, and the provision of mammography screening services. The media campaign and educational outreach activities aimed to modify behavior through mass-media education, disseminating breast cancer prevention messages often reinforced by peer role models, as well as by employing an outreach coordinator to educate small businesses, churches, and community groups in the targeted zip codes of the program. Simultaneously, the patient navigation and mammography service components intended to remove social, cultural, and economic barriers by supporting patients through the screening system and providing free services for eligible women.From 2013 to 2016, University Health System (UHS)—the Bexar County Hospital District—implemented the Approximately 2100 women age 40 and older from our target populations were navigated through the program. In addition, the mammography screening rate for the target population increased from 60% to 80% throughout the program.A Su Salud program. Individuals recruited to participate in the focus groups included any individual that received or was currently receiving services through the UHS A Su Salud Breast Health Program. Additional eligibility criteria included participants fluent in English or Spanish and willing to speak openly in a group setting about their experience with the program. Patient navigators contacted individuals who participated in the program during any of the three program years through telephone to recruit them to participate in the focus groups. All eligible individuals received mailed postcard invitations to participate in the focus groups. If interested, individuals called the phone number provided on the post card to connect with a navigator. Individuals who agreed to participate received information from patient navigators about the location and time for their focus group, assigned according to the participant's preferred language. All focus group participants received a gift card as compensation for their time and participation.Focus groups assessed the knowledge, attitudes, and behaviors of participants, as well as their overall experience, utilizing the breast cancer services from the Five focus groups consisting of 30 total participants were conducted in April 2016. Two focus groups were conducted in English, and three focus groups were conducted in Spanish. Each focus group was recorded and transcribed. Using an iterative method of content analysis utilized frequently in qualitative research, researchers coded transcripts for themes using NVivo 11 software. Before coding, each reviewer first read the transcripts in their entirety to allow for ideas and concepts to develop. Each of the two reviewers identified emerging themes individually and met to discuss initial findings. After initial discussions, reviewers went through transcripts to identify specific subthemes. After several meetings, reviewers finalized the code tree and codebook utilized for coding the focus group transcripts. The code tree and codebook served as a tool to allow for better agreement between coders. Throughout the coding process, coders communicated to discuss challenges, emerging themes, and agree on conversations that were difficult to code. Intercoder reliability found 0.78 percent agreement among coders. The project received Institutional Review Board approval from University of North Texas Health Science Center.A Su Salud Breast Health Program. We modeled the natural history of breast cancer based on a synthesis of evidence from existing breast cancer simulation models. In particular, we developed the main model structure and estimated disease progression transitions based on a model developed by researchers from the University of Minnesota and the University of California, San Francisco.18 Data on program costs, population characteristics , and screening rates before and after the program implementation were collected from the breast health program. Other model parameters  were obtained from other existing literature.19–22 We also reviewed the Cancer Intervention and Surveillance Network (CISNET) breast cancer models to ensure that our model structure and parameter estimation reflect the state of the art in breast cancer disease progression modeling.23–25We used a stochastic microsimulation model to evaluate the long-term health and economic consequences associated with the A Su Salud program and status quo), which would help reduce stochastic uncertainties and provide us with stable estimates of long-term outcomes. We reported lifetime costs and QALYs and discounted them by 3% annually. A discount rate of 3% is commonly used in cost-effectiveness analysis in health care and medicine.26 We also reported life expectancy and the incremental cost-effectiveness ratio (ICER). An ICER measures the additional cost resulting from the breast health program that must be incurred to gain an additional QALY relative to the status quo. As a common standard in the United States, we used $50,000 per QALY as a threshold to determine cost-effectiveness of the studied program.27 In other words, a program is considered cost-effective if it has an ICER less than $50,000 per QALY compared to the status quo. Detailed model documentation is available upon request.The microsimulation model captured the potential breast cancer disease progression for a simulated population cohort and calculated discounted costs and quality-adjusted life years (QALYs) throughout their lifetime. We simulated a large population cohort  for two scenarios modeled Many focus group participants stated that they obtained information about breast health from a health care professional or from the Internet. Participants also discussed their depth of knowledge regarding breast cancer screening, including mammograms and breast self-examinations. Although most of the participants indicated that they had the knowledge to perform a self-examination or knew where to obtain an examination, many of them requested additional information related to the frequency of screening, as well as the age to initiate mammograms:Risk factors for breast cancer were also discussed by participants within each focus group, with heredity being the most-often mentioned risk factor for breast cancer. How breast cancer develops was another topic that was a discussion point during the focus groups. Some of the participants indicated that breast cancer develops in the breast tissue and by cancer cells that are in the body. Some of the other participants were unsure and requested additional information.Attitudes regarding breast health were assessed according to whether or not participants favor or disfavor mammograms and breast self-examinations, as well as views on susceptibility for developing breast cancer. Overall, most of the participants had a positive attitude toward both mammograms and breast self-examinations. Many of the participants who desired screening indicated that they were more susceptible to developing breast cancer due to their family history, preexisting conditions, ethnicity, lifestyle, or current health status. Those participants relating a distaste toward screenings indicated that cultural beliefs, fear of associated pain, false negatives, and apprehension led them to avoid screenings.I think that the positive is that we're avoiding getting sick from cancer if we find it in time. The negative is a lot of people don't do it and when they do it it's already late and they don't have a cure and it's when the person dies. So to avoid that I say we have to be checking ourselves, go to the doctor, do our exams every day not only or say yes I'm going to do it once a week because you never know. Everybody is different, every person is different. I think that the most important is to get checked to avoid the negative that it's too late. That's what I say. (Participant)I believe there is a racial significance that we get breast cancer, the darker the skin, the more susceptible we are, at least that's what I think I read. That racial barrier or racial consequences do play a part in getting breast cancer. (Participant)Participants also offered insight on how elements of their personal life such as cultural and generational norms, family practices, experiences, and preferences influence their assessment of breast health services and screening behaviors. Of all the factors considered, culture was repeatedly mentioned as one important determinant of breast health knowledge, breast health screening behavior, and the likelihood of developing cancer. Focus group participants believed that there is a lack of information within their own culture that may be due to embarrassment regarding the topic, which results in the lack of discussion about breast health. Participants also reported believing that Hispanic women were not as concerned about taking care of themselves or visiting the doctor. Overall, participants believed that minority women, specifically Hispanic, African American, and Muslim women, were more susceptible to developing breast cancer than their White counterparts:Participants offered their views on their individual health behavior practices, including information sharing, need for lifestyle changes, and intention to continue obtaining breast cancer screening. Most of the participants agreed that they discuss any information regarding their breast health with their health care provider. Some focus group participants also anticipated making a lifestyle change as a result of program participation. Lifestyle changes mentioned included losing weight, modifying diet, becoming more active, and quitting smoking.It's not just the diet. It's lifestyle changes. When I was saying exercising, I wasn't talking about food, I was talking about lifestyle choices. How much do you drink? How much do you smoke? Or do you have to do those things? Those are some of the things that we can modify and that's what the screenings are about. It's not just, “we are going to check your breasts.” These are the things that will put you in a risk factor, for not just cancer of the breast, but other problems. (Participant)A Su Salud Breast Health Program. The A Su Salud Breast Health Program would cost an average of $124.80 more than the status quo per person over the lifetime of participants. The program would increase the life expectancy of the population cohort by 0.71 years and increase QALYs by 0.04 years. The ICER for the breast health program relative to the status quo was $3120 per QALY. Since $3120 per QALY is less than the $50,000 per QALY threshold, we conclude that the breast health program is highly cost-effective under the parameter assumptions described above.A Su Salud Breast Health Program. We also studied the robustness of the baseline results by conducting sensitivity analyses that account for uncertainties in the costs and effectiveness of the A Su Salud Breast Health Program and programs like it may be able to increase the mammography screening rate and life expectancy of Hispanic women and other vulnerable populations by improving patient knowledge, attitudes, and behaviors regarding breast health. Across focus group responses, participants indicated that they improved their knowledge of risk factors, available resources, and general health practices, which are important for breast cancer screening adherence. Participants also acknowledged that many impediments to care—such as fear of procedure(s), false negatives, and cultural beliefs—were lessened as a result of program participation. Participant behaviors also improved; of those who responded to a question related to their intention to maintain breast health screenings, all replied affirmatively. These findings provide a comprehensive picture of the reasons why the screening rate was increased significantly due to the breast health program.The findings from our study suggest that the Results from the cost-effectiveness analysis further showed that the program was effective in increasing the life expectancy and quality of life for program participants in a cost-effective manner. Specifically, the breast health program could increase the life expectancy of each participant by 0.71 years while the cost of the program could increase up to five times before the program is no longer cost-effective.29 In these previous studies, we showed that patient navigation programs targeting cervical cancer and colorectal cancer were also cost-effective due to significantly reduced cancer risk.29Similar multilevel, community-based patient navigation programs have been shown to improve the cervical cancer screening rate of Hispanic women and the colorectal cancer screening rate of Hispanic men.29 providing the underserved Hispanic population with more knowledge about cancer risk factors and reducing the prevalence of cancer fatalism within this population.11 They also used principles from behavioral economics—such as providing personalized risk assessment to address unrealistic optimism of some patients and reminding patients of their missed appointments —to engage participants and increase cancer screening uptake and adherence.29 Behavioral economics, which emphasizes the effects of psychological, social, and cognitive factors on the decisions of individuals, has demonstrated great promise in improving cancer screening for underserved populations.15 Based on these promising results, health care providers may consider developing and testing similar programs in other places or for other populations of interest to improve cancer screening uptake and reduce disparities among underserved populations.These programs share several elements in common: they were culturally sensitive and comprehensive,There are a number of limitations in this study. First, interview participant responses may be intentionally or unintentionally biased. Second, evaluation interviews are not generalizable to the larger population outside of UHS because random sampling methods were not used to select participants. Third, the breast cancer natural history model was developed based on parameters that reflect the general U.S. population, not specifically the target population of this study. We have conducted sensitivity analyses to account for the uncertainty in simulation results. We will further improve the results as more local data for model parameterization become available and conduct more comprehensive sensitivity analyses.In conclusion, this study has demonstrated the effectiveness and cost-effectiveness of a multilevel, community-based patient navigation program implemented by a health system serving a low-income Hispanic population. The program successfully integrated a health promotion media campaign, educational outreach, a patient navigation initiative, and the provision of mammography screening services. Our positive findings indicate that the patient navigation program could be translated to other socioeconomic and demographic groups. Future work needs to be done to better understand which of the behavioral change components in the program are most promising.


Models that accurately predict risk of breast cancer are needed to help younger women make decisions about when to begin screening. Premenopausal concentrations of circulating anti-Müllerian hormone (AMH), a biomarker of ovarian reserve, and testosterone have been positively associated with breast cancer risk in prospective studies. We assessed whether adding AMH and/or testosterone to the Gail model improves its prediction performance for women aged 35–50.In a nested case-control study including ten prospective cohorts (1762 invasive cases/1890 matched controls) with pre-diagnostic serum/plasma samples, we estimated relative risks (RR) for the biomarkers and Gail risk factors using conditional logistic regression and random-effects meta-analysis. Absolute risk models were developed using these RR estimates, attributable risk fractions calculated using the distributions of the risk factors in the cases from the consortium, and population-based incidence and mortality rates. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory accuracy of the models with and without biomarkers.The AUC for invasive breast cancer including only the Gail risk factor variables was 55.3 . The AUC increased moderately with the addition of AMH , testosterone , or both . The largest AUC improvement (4.0) was among women without a family history of breast cancer.AMH and testosterone moderately increase the discriminatory accuracy of the Gail model among women aged 35–50. We observed the largest AUC increase for women without a family history of breast cancer, the group that would benefit most from improved risk prediction because early screening is already recommended for women with a family history.The online version of this article (10.1186/s13058-019-1126-z) contains supplementary material, which is available to authorized users. While women aged 50 are uniformly included in mammographic screening recommendations, the guidelines regarding at what age to start screening are inconsistent, varying from age 40 to 50, particularly for women without a family history of breast cancer (reening1 ). Improvreening1 –13. NoneThe Gail model 2  is the mWe recently showed that the premenopausal circulating concentration of anti-Müllerian hormone (AMH), a marker of ovarian reserve, is associated with risk of breast cancer . CirculaThe objective of this study was to evaluate whether adding circulating AMH and/or testosterone measurements to the Gail model improves its discriminatory accuracy among women aged 35–50.Participants in a nested case-control study in a consortium of ten prospective cohorts from the USA, UK, Italy, and Sweden  were incIncident breast cancer cases were ascertained by each cohort through self-report on follow-up questionnaires and/or linkages with local, regional, or national cancer registries. All cases of incident invasive breast cancer diagnosed among women who were 35–50 at the time of blood donation were included except in the NHS cohorts, which further limited case selection to women who were premenopausal and between the ages of 35–50 at diagnosis. Controls were selected within each cohort using incidence density sampling. One control was selected for each case . Matching variables included age and date of blood donation, and race/ethnicity . Many ofAMH was measured in serum or plasma samples from women who were premenopausal at the time of blood donation using the picoAMH assay (ANSH laboratories) . Women wTotal testosterone was measured for all subjects in CLUE II, NHS, and NSMSC and for the matched sets for which it was not measured previously for the other cohorts. Measurements were done in the Immunochemical Core Laboratory of the Mayo Clinic by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Assay coefficients of variation (CVs) were calculated using blinded quality control samples. For AMH, the mean intra-batch CV was 5.1% and the inter-batch CV was 21.4%. For testosterone, all intra- and inter-batch CVs were ≤ 10.6%. Previous testosterone measurements were performed as described in , 45–48.I2 and Q-tests were used to test for heterogeneity across cohorts.We estimated cohort-specific relative risks (RRs) associated with the breast cancer risk factors included in the Gail model and with each of the biomarkers (testosterone and AMH) using conditional logistic regression (odds ratio estimates are referred to throughout as relative risks (RRs), by convention). Cohort-specific RRs were combined to obtain consortium-wide RR estimates using the random-effects meta-analytic method. We used the same coding as the BCRAT for age at menarche  and age at first live birth  [We used the method described by Gail et al. , 49 to ehttps://dceg.cancer.gov/tools/risk-assessment/bcrasasmacro), which uses US population-based RR estimates [For comparison, we also calculated the 5-year absolute risks of developing breast cancer using the BCRAT SAS macro  based on the 5-year absolute risk estimates from the BCRAT, the Gail model, and the Gail model with addition of AMH and/or testosterone. Summary AUCs were estimated from the cohort-specific AUCs using random-effects meta-analytic methods. AUCs were also estimated within subgroups, i.e., by age, estrogen receptor (ER) status of the tumor, and Gail risk score (< 1%/≥ 1%), and for women without a family history of breast cancer. AUCs are expressed throughout as percentages (AUC × 100) for ease of interpretation. Finally, we assessed reclassification of 5-year absolute risks upon addition of biomarkers.Descriptive characteristics of the cases and controls are shown in Table Table p = 0.007) and the model including both AMH and testosterone , but not testosterone alone . AUCs were similar when both in situ and invasive cases were considered together . Overall, inclusion of biomarkers tended to moderately increase 5-year risk estimates for cases and reduce estimates for controls.https://www.uspreventiveservicestaskforce.org/Page/Document/UpdateSummaryFinal/breast-cancer-screening1).The increase in AUC resulting from the addition of biomarkers was slightly higher in analyses limited to women without a family history of breast cancer than that observed in analyses including all women. This is of interest because the majority of breast cancers occur among women without a family history. Further, women without a family history are the group in which improvements in risk prediction could have the most impact, since it is already recommended that women with a family history start screening early  , but notIn addition to the large size of our study, its major strength is the prospective design. Samples collected prior to diagnosis are valuable for measuring biomarkers that can be affected by the diagnosis and/or treatment of breast cancer. Another strength is that detailed epidemiological data on breast cancer risk factors were collected from all cohorts.In conclusion, we observed moderate increases in the discriminatory accuracy of the Gail model 2 for women aged 35–50 with the addition of AMH and testosterone. Combining these markers with others  may improve risk prediction models, though the improvement in discriminatory accuracy will remain limited until new markers with stronger associations with breast cancer risk are identified , 81.Additional file 1:Table S1. Breast cancer incidence and competing mortality rates used for each cohort to estimate absolute risk. Table S2. Descriptive characteristics of invasive + in situ cases and matched controls. Table S3. Descriptive characteristics of invasive breast cancer cases and matched controls, by cohort. Table S4. Descriptive characteristics of invasive plus in situ breast cancer cases and matched controls, by cohort. Table S5. Random-effects meta-analysis relative risk estimates, invasive and in situ. Figure S1. Cohort-specific and random-effects meta-analysis relative risk estimates for Gail model variables, AMH and testosterone (invasive cases only). Figure S2. Cohort-specific and random-effects meta-analysis relative risk estimates for Gail model variables, AMH and testosterone, invasive and in situ. Figure S3. Relative risk estimates by age group, invasive cases only. Figure S4. AUCs by cohort 95% confidence intervals, invasive and in situ. (DOCX 254 kb)


This study consisted of 764 cases with cervical intraepithelial neoplasia (CIN)1, 2, 3, or cervical cancer, and 729 controls from six gynecologic oncology clinics in South Korea. The DII was computed using a validated semiquantitative Food Frequency Questionnaire (FFQ). Odds ratios and 95% CI were calculated using multinomial logistic regression. Higher DII scores were associated with higher cervical carcinogenesis risk. A significant association was observed between the DII and risk among CIN2/3  and cervical cancer patients . Among Human Papilloma Virus (HPV)-positive women, a significant association was found between DII and cervical carcinoma risk with CIN2/3 . Moreover, women with CIN2/3 and cervical cancer showed a significant association with proinflammatory diet in people without of physical activity . These findings suggest that high intake of proinflammatory diets is associated with increased risk of cervical carcinogenesis among women with CIN2/3. Further evaluation in future studies to confirm this association is warranted.Several studies have reported that diet’s inflammatory potential is related to chronic diseases such as cancer, but its relationship with cervical cancer risk has not been studied yet. The aim of this study was to investigate the association between Dietary Inflammatory Index (DII Cervical cancer is a life-threatening gynecologic malignancy arising in the lining of the cervix in women, particularly in less developed and developing countries . It is ePrevious studies reveal that diet is associated with both cancer and inflammation ,11,12,13®) describes a dietary pattern that classifies an individual’s diet on a continuum ranging from anti-inflammatory to proinflammatory [The Dietary Inflammatory Index  ,24,25,26The distribution of the demographic characteristics of the patients with cervical intraepithelial neoplasia (CIN) grades 1, 2, or 3, and cervical cancer, along with those of the controls, are presented in ®) among the 764 cases and 729 controls is shown in The distribution of the selected food parameters of the Dietary Inflammatory Index (DIIp-trend = 0.0005). High consumption of proinflammatory dietary parameters was not associated with increased risk among women with CIN1 . In the fully adjusted model, cervical cancer women in the highest quintile had a statistically significant increased risk of cervical cancer in comparison with the lowest quintile . When the DII was used as a continuous measure, significant results were obtained among CIN2/3 cases but not for CIN1  or cervical cancer (OR = 1.12 95% CI = 1.00–1.24) cases. Among the CIN2/3 patients, one unit increase in DII was associated with a 23% increase in the odds of cervical cancer risk .The odds ratios of cervical cancer risk and the corresponding 95% confidence intervals according to quintiles of DII among the cases and controls are presented in p-trend = 0.03), despite the insignificant results obtained for HPV-positive CIN1 cases  and HPV-positive cervical cancer patients  with nonsignificant trends. The p-value for interaction was not significant for DII and HPV infection. When the DII was expressed as a continuous measure, in HPV-positive women, similarly significant associations were found between DII and cervical cancer risk with CIN 2/3 . Among the HPV-negative women, when the DII was expressed as quintiles, significant associations were found between proinflammatory diet and risk among CIN2/3 cases . When the DII was used as a continuous measure, significant associations were observed among HPV-negative women consuming the most proinflammatory diet . No associations were observed between DII and cervical cancer risk among HPV-negative women with CIN1 and cervical cancer.p-trend ≤ 0.0001) and cervical cancer cases  (p-trend = 0.008) with no physical activity. Similarly, the results obtained for using DII as a continuous variable in relation to cervical cancer risk showed significant associations for women with CIN 2/3  and cervical cancer  with no physical activity. Among the participants performing physical activity, no associations were found between proinflammatory diet and risks of CIN grades 1, 2, and 3 and cervical cancer. The p-value for interaction was not significant for the DII and physical activity.The association between the DII and risk was significant among CIN 2/3   and CIN risk. We observed that higher DII scores, as indicative of more proinflammatory diets, were associated with increased risk of CIN. Particularly, women with higher DII scores were at higher risk of CIN2/3. HPV-positive women showed a strong relationship between DII dietary patterns and cervical cancer risk by the presence of cervical intraepithelial lesions. Moreover, physical activity was significantly associated with DII and risk among CIN2/3 and cervical cancer. Thus, the results confirmed the study hypothesis, which is to say, that higher DII scores are associated with increased risk of cervical carcinogenesis.Although this is the first attempt to examine the association between DII and cervical carcinoma risk, several investigations into the association between dietary patterns and CINs, which leads to cervical cancer, revealed similar results. A cross-sectional study conducted in Italy reported that Western dietary patterns, which include large amounts of trans fat, sodium, protein, cholesterol, saturated fat, and carbohydrate, were significantly associated with high incidence rates of cervical cancer . SimilarThe current study found a statistically significant association between DII and cervical carcinogenesis risk among women with CIN2/3 and cervical cancer. However, the results were not significant for women with CIN1. There are no previous reports on any association of DII with cervical cancer risk, and moreover, there are few reports of evidence for any significant association between DII and inflammatory biomarkers of cervical cancer and their differences in disease progression . AccordiA statistically significant association between DII and cervical cancer risk was revealed among HPV-positive women with CIN 2/3. An earlier study reported that changes in dietary patterns for high(er) intakes of green tea and vegetables were considered as a protective factor for CINs and cervical cancer . AccordiAnother important finding of the present study was that women not engaging in regular physical activity had increased risk of CIN2/3 and cervical cancer by consumption of a proinflammatory diet. A previous study reported that physical activity was inversely associated with CIN 2/3 and cervical cancer . PhysicaThe DII score was developed to assess the inflammatory potential of diet. Two studies have obtained data on 29 and 31 food parameters for DII development, respectively ,26. In tThe major strength of the present study is the fact that it is the first to explore the association between DII and cervical cancer risk among Korean women. Moreover, the study includes a large sample size with a valid FFQused to assess dietary data. Another strength is the use of DII scores to access the inflammatory potential of diets, because this approach takes into account both proinflammatory and anti-inflammatory food parameters that characterize human diet. Furthermore, significant results obtained in this study underline the importance of consuming an anti-inflammatory diet to prevent cervical carcinoma risk. Notwithstanding such strengths, this study should be considered in view of its limitations. As a case-control design for a Korean population, there was a possibility of selection and information bias based on dietary habits and health concerns. Another limitation is that only 33 of the 45 food parameters were taken for DII development. So, the remaining 12 food parameters, if also utilized, could have influenced the results. Additionally, in this study, the levels of inflammatory biomarkers were not measured, because disease progression severity was evaluated by the presence of well-known histopathological parameters.This hospital-based case-control study was conducted at The National Cancer Center (NCC) of Korea from March 2006 to 2010. It was approved by the ethics committees of NCC and of each of the pertinent centers (IRB: NCC2016-0147). The study included 764 cases and 729 controls. These women had been selected randomly from the gynecologic oncology clinics of six university hospitals in Korea. Among the 764 cases, 319 were diagnosed with CIN1, 216 with CIN2/3, and 229 with CX CAN. Details on the study’s design criteria are available in our previous paper . The casFood Frequency Questionnaire (FFQ)-derived dietary data were used to calculate the DII scores for all of the participants. The FFQ consists of dietary data on 95 food and beverage items and queries on frequency and portion size for each item . We recoA complete description of the DII is available elsewhere . Brieflyn = 1), educational status (n = 3), income (n = 2), smoking (n = 2), alcohol consumption (n = 3), physical activity (n = 6), pregnancy (n = 5), oral contraception (n = 4), and menopausal status (n = 3).During the collection of the participants’ socio-demographic characteristics, age, BMI, and energy intake were considered as continuous variables. Marital status, education level, income, smoking, drinking, physical activity, pregnancy, oral contraceptive usage, and menopausal status were considered as categorical variables. Marital status was further classified into single, married, and divorced. Education level was classified as middle school or below, high school or below and college or above. Similarly, menopausal status was classified into pre- or peri-menopause  and post-menopause . Smoking , drinking , physical activity , and oral contraceptive usage participants  were classified accordingly. The number of missing data for categorical variables were as follows, marital status .The DII quintiles were defined by the distribution of DII scores in the control group. The medians and 25th and 75th percentile values were calculated for the continuous variables, and frequencies with percentages were calculated for the categorical variables. The Jonckheere-Terpstra and Mantel-Haenszel tests were used to compare the categorical and continuous variables, respectively. The distribution of DII components was estimated among the groups using the Kruskal-Wallis and Jonckheere-Terpstra tests. The odds ratios (OR) and corresponding 95% confidence intervals (CI) were estimated using multinomial logistic regression. The minimally adjusted model was adjusted for age and energy intake; the fully adjusted model was additionally adjusted for marital status, education, smoking, alcohol consumption, physical activity, pregnancy history, use of oral contraception, menopausal status, and BMI. Stratified analyses were carried out by HPV status and physical activity. To investigate the DII components and the risk of cervical cancer progression, the intake foods were adjusted for energy density. P for trends were calculated using quintiles of DII. The results of this study support a significant association between proinflammatory diet and cervical carcinogenesis risk, especially in women with CIN2/3. Also, a significant association was observed in HPV-positive women with CIN2/3. Intake of more anti-inflammatory dietary factors, such as omega-3 fatty acids, plant-based foods rich in fiber, beta carotenes, and phytochemicals, with reduced intake of proinflammatory factors such as fried foods or processed foods rich in saturated fat or trans fatty acids, might be a strategy for mitigating the risk of some types of cervical cancer. Further evaluation in future studies will help to improve the present understanding of the association between DII and cervical cancer risk and of healthy diets that regulate the level of inflammatory biomarkers; thereby, cervical carcinogenesis risk will be reduced and public health will be promoted.