Breast cancer risk was high in the PEA, high income, and no history of exercise groups, but significantly reduced in the regular-use-of-analgesics group. Notably, the working age group of 40~49 years, within the PEA group, had the highest HR of breast cancer development ; whereas regular analgesic use in those aged 25~39 years decreased breast cancer risk . In conclusion, our results suggest that individuals at a high-risk of comorbidity may benefit from regular use of analgesics, which may prove to be a useful strategy for breast cancer prevention in the Young-aged group.We assessed the association between breast cancer and analgesic use in women of a specific working-age group. The Korean National Health Insurance Service–National Sample Cohort database (KNHIS–NSC) data were analyzed. We calculated hazard ratios (HRs) with 95% confidence intervals (CIs) for patients’ cancer risk based on whether the women participated in economic activity (PEA or not PEA (NPEA) groups) and analgesic use. Additionally, breast cancer incidence variations by age group, and PEA or NPEAs, health behavior, Charlson Comorbidity Index, and analgesic use were evaluated. The PEA group had a higher cancer risk than the NPEA group (HR = 1.542, 95% CI: 1.345–1.768,  The participation rate of women in economic activities in the Organization for Economic Co-operation and Development (OECD) member countries increased from 57.0% in 1991 to 63.6% in 2016 . In AsiaThe proportion of women participating in economic activities in Korea has constantly increased from 47.4% in 2003 to 50.8% in 2017. There are variations in the pattern of participation in economic activities among Korean women depending on the age group. Between the ages of 25~39, pattern of participation in economic activities (including employment rate) shows a decrease due to marriage, childbirth, and childcare; increase between ages 40~49, when women return to work; and significant decrease again in women over the age of 50 [Furthermore, economic-activity-related stress and chronic inflammation have a causal relationship with breast cancer incidence among women . In a stHowever, few studies have assessed the cancer-inhibiting effect of analgesics, especially among working age groups with different characteristics of social participation, nor whether economic activity and analgesic use are related to breast cancer development. Thus, this study aimed to analyze the association between economic activity, regular analgesic use, and breast cancer development in women of specific age groups, to provide a basis for improving cancer prevention practices.2), and 1531 women (64.1%) had no alcohol intake. For the Charlson Comorbidity Index (CCI) score, 1515 (63.4%), 573 (24.0%), and 94 (3.9%) had scores of 0, 1, and ≥3, respectively. Regarding analgesics use, 1572 (65.8%) were non-regular users, 536 (22.4%) were non-users, and 282 (11.8%) were regular users of analgesics.The participant characteristics are presented in 2), and 2220 women (86.7%) had low total cholesterol (<240 mg/dL). For the CCI score, 1392 (54.4%), 641 (25.0%), and 226 (8.8%) had scores of 0, 1, and ≥3, respectively. Regarding analgesics use, 1630 (63.7%) were non-regular users, 600 (23.4%) were non-users, and 330 (12.9%) were regular users of analgesics.In the Middle-aged group, 1125 (43.9%) were family members of the self-employed, 825 (32.2%) were dependents of the employee-insured, and 400 (15.6%) were self-employed; In this group 1000 (39.1%) had a high income, 817 (31.9%) had a low income, and 743 (29.1%) had a middle income. Regarding the health behavior variables, 1756 women (68.6%) had a low BMI (<25 kg/m2), and 945 women (52.9%) never exercised (Never). For the CCI score, 639 (35.8%), 268 (26.2%), and 385 (21.6%) had scores of 0, 1, and ≥3, respectively. Regarding analgesics use, 1013 (56.8%) were non-regular users, 473 (26.5%) were non-users, and 299 (16.8%) were regular users of analgesics  were dependents of employee-insured, 500 (28.0%) were family members of self-employed, and 295 (15.6%) were self-employed; 679 (38.0%) had a high income, 611 (34.2%) had a low income, and 495 (27.7%) had a middle income. Regarding the health behavior variables, 1026 women (57.5%) had a low BMI . The risk of breast cancer was higher in the high-income group than in the low-income group . The risk of breast cancer was lower in participants who reported exercise than in those who did not report exercise . It was also lower in participants with regular analgesic use compared to those with no analgesic use   had a higher risk of breast cancer than the inactive group (NPEA group)  .In the NPEA group, the 2- and 12-year cumulative incidence rates of breast cancer for never-users of analgesics were 1.4% and 27.2%, respectively. For non-regular users of analgesics, the rates were 1.6% and 25.6%, respectively. For regular analgesic users, the rates were 1.3% and 21.8%, respectively a.In the PEA group, the 2- and 12-year cumulative incidence rates of breast cancer for never-users of analgesics were 1.3% and 29.4%, respectively; for non-regular users, 1.1% and 24.7%, respectively; and for regular analgesic users, 1.3% and 19.7%, respectively .p < 0.05). The HR of those at the middle income level was 1.731 . The HR of CCI score = 1 was 1.234 , CCI score = 2 was 1.555 , and CCI scores ≥3 was 2.095 . Compared with no analgesic use, regular use was associated with a lower risk of breast cancer .Among the patients in the Young-aged group (25~39 years), the risk of breast cancer was higher in the PEA group than in the NPEA group . Among the patients in the Senior-aged group (50~64 years), those who participated in economic activity had a significantly higher risk of breast cancer , compared with those with no economic activity. Regular analgesic use  was associated with a significantly lower risk of breast cancer (Among the patients in the Middle-aged group (40~49 years), compared with the non-working group, the working group had a significantly higher risk of breast cancer  group had a higher risk of breast cancer than the non-PEA (NPEA) group. In particular, there was a notable difference in the cancer risk of women in specific working-age groups, based on their characteristics of social participation. Those in the Middle-aged group (40~49 years) who participated in economic activity had the highest risk of developing breast cancer.Long working hours adversely affect the health of female workers because working-age women are also involved in other responsibilities such as housework and childcare . The anaStress is known to be higher in the PEA group than in the NPEA group. Stress increases the level of chronic inflammation, and chronic inflammation can lead to cancer development . Stress Not only preventative health behaviors such as exercise, but also cancer prevention practices were found to be lower in the PEA group than in the NPEA group ,13. ThroAlthough the survival rate of breast cancer has significantly increased with the development of medical technology, the incidence rate of breast cancer is high in premenopausal women in South Korea. Thus, policies are also needed to alleviate the burden of unstable work and childcare for women in the 40~49 age group.The second finding was the significant association between the use of analgesics and the risk of breast cancer. Regular analgesic use was associated with a lower risk of developing cancer. The use of NSAIDs such as aspirin and ibuprofen has been reported to reduce the risk of cancer and mortality . In a muAmong the Young-aged group (25~39 years), working and high-risk comorbidity (CCI) were associated with a higher risk of cancer, and regular use of analgesics was associated with a lower risk. Epidemiological studies have repeatedly indicated an association of high-risk comorbidity (CCI), particularly obesity, hypertension and diabetes, with risk of breast cancer ,27,28. IThe Journal of the American Medical Association (JAMA) published guidelines that stated that aspirin should not be taken, even at low doses, because it leads to an increased risk of gastrointestinal and cerebral hemorrhage in those aged over 60 years [In addition, we confirmed that regular use of analgesics in the Young-aged group (25~39 years) has an inhibitory effect on cancer development. The drug effect of analgesics was more significant in relatively young women than in older women. Aspirin is an anti-inflammatory analgesic that is known to be effective in preventing myocardial infarction and stroke, and lowering the mortality rate due to cancer ,32. Rece60 years . However60 years . As suchThis study has the following limitations. First, we did not consider occupation type, business category, and work duration. Second, selection bias may exist since economic activity (PEA or NPEA) is classified by insurance type. Third, confounding variables that increase the risk of breast cancer, such as family history, childbirth status, and contraceptive use, were not controlled. Fourth, in the case of alcohol intake and smoking, intensity, quantity, and duration are important, not the experience, such as ever or never . HoweverHowever, despite these limitations, this study is significant in that it analyzed the association between the use of analgesics and the occurrence of breast cancer in working women by classifying them into Young-, Middle-, and Senior-aged groups. Since there may be differences in the cancer prevention effect of non-opioid analgesics (acetaminophen and NSAIDs) depending on the mechanism of action of the analgesic, care should be taken in interpretation. In the future, it is expected that additional studies will be needed in consideration of the limitations of this study.Population-based cohort data was used from the Korean National Health Insurance Service–National Sample Cohort database (KNHIS–NSC) for the period 2002–2013 . Based oIn this study, sampling was carried out as follows: Cancer was defined as the presence of the same C code  more than three times  (DefinitPatients with cancer before the index date (n = 814) and males were excluded (n = 18) from this study. Furthermore, patients aged <25 years (n = 60) and >65 years (n = 228) at the index date were excluded. Since the CCI is calculated based on the medical information one year before the date of the first health examination, patients whose date of first health examination was in 2002 were excluded (n = 449). Medical aid program recipients whose economic activity (PEA or NPEA) status could not be confirmed (n = 50), or who had missing health examination data were also excluded (n = 964). To select newly diagnosed cancer patients during the cohort monitoring period, patients with a follow-up period <2 years were excluded (n = 57) .We followed up factors influencing cancer incidence based on independent cohorts of three groups: Young-aged, Middle-aged and Senior-aged cohorts. Participants who were not diagnosed with breast cancer for each age cohort were selected in a 1:4 ratio, considering their residence (metropolitan or non-metropolitan area) and insurance type. There were 2390, 2560, and 1785 participants in the Young-aged, Middle-aged, and Senior-aged cohorts, respectively. Overall, 6735 participants were included in the analysis .In this study, age , economic activity status , health behavior , CCI, and analgesics prescription behavior were analyzed. The baseline age, economic activity variables corresponding to the time of entry into the cohort, and health behavior variables, were based on the date of the first health examination. CCI was calculated based on the medical records, one year prior to the date of the first health examination. Analgesic use (regularity) was calculated from prescription records from the time of cohort entry to the 2-year follow-up .2; <25 or ≥25), total cholesterol  [Age was categorized into three groups  based on economic participation; social participation characteristics were different among the groups. In the Senior-aged group , we examined the characteristics of the elderly who were economically active. The economic activity variables included insurance type  and income quantile  [or ≥240) , self-reor ≥240) . CCI wasor ≥240)  defined or ≥240) . The CCIThis study explored the inhibitory effect of cancer occurrence through medication compliance (regularity) based on WHO 3-step ladder analgesics, which are indicated for pain of various causes, such as occupational stress, anxiety/depression (mood disorder), diabetes, hypertension, and obesity, etc. ,46. WHO We approached this study from the perspective of medication compliance  for the purpose of controlling for pain caused by various causes (before cancer occurrence).In this respect, non-opioid analgesics were evaluated comprehensively without classification according to the expression of cyclooxygenase (COX). The first-step analgesics considered included acetaminophen, aspirin, piroxicam, diclofenac, celecoxib, ibuprofen, naproxen, mefenamic acid, ketoprofen, dexibuprofen, and others.Analgesic use was determined by extracting the prescription history corresponding to first-step analgesics for 2 years from the baseline. Regular use was defined as prescription for >15 days per month for >6 months . No use The differences in economic activity, health behavior, CCI, and first-step analgesic use in each working-age group were examined using the chi-square test. We calculated HRs with 95% CIs for patient risk of developing cancer based on economic activity (NPEA or PEA) and analgesic use (Model 1). Additionally, we determined the risk of cancer by specific age group (Model 2) using a proportional hazards regression model. All statistical analyses were performed using the statistical package R version 4.2 .The results of this study showed that the risk of breast cancer was high among those who engaged in economic activity, had a high income, and no history of exercise in all working-age groups. Notably, participants in the Middle-aged group (40~49 years) who engaged in economic activity had the highest HR of breast cancer development. Therefore, since women aged 40~49 years account for a major portion of the economic population, policies are required to reduce their burden of childcare and unstable employment, and ameliorate their health behavior. Particularly, attention should be paid to a lack of exercise among these women. Furthermore, regular analgesic use may be beneficial for inhibiting cancer development in the Young-aged group (24~39 years). Our results suggest that the group at high-risk of comorbidity may benefit from the regular use of analgesic, which may prove to be a useful strategy for breast cancer prevention in the Young-aged group.


At what age should women with varying levels of breast cancer risk start screening?In this cohort study of 1 549 988 women in China, participants were divided into groups according to breast cancer risk scores . Using the 10-year cumulative risk of breast cancer at age 50 years in the general population as a benchmark, the optimal starting age of screening was identified as 43, 48, or after 55 years for women with high, medium, or low risk, respectively.These results identify the risk-adapted starting age at which to begin screening in women with varying levels of breast cancer risk in China, which may inform updates of current screening guidelines. This cohort study explores the use of a risk-adapted starting age for breast cancer screening in China, considering breast cancer risk factors in community-dwelling women aged 40 to 74 years with different risk levels. Although current guidelines highlight the need for earlier screening in women at increased risk of breast cancer in China, data on risk-adapted starting ages of screening are limited.To explore the risk-adapted starting age of breast cancer screening in China, with comprehensive consideration of breast cancer risk factors.A multicenter community-based cohort study was conducted under the framework of the Cancer Screening Program in Urban China. Data were collected from January 1, 2013, to December 31, 2018, for unscreened community-dwelling women aged 40 to 74 years without a history of cancer, kidney dysfunction, or severe heart, brain, or lung disease. Data analysis was performed from October 1, 2021, to August 16, 2022.Baseline characteristics associated with breast cancer, including first-degree family history of breast cancer, benign breast disease, breastfeeding, age at menarche, and body mass index.Outcomes included breast cancer diagnosis and age at diagnosis. Risk-adapted starting age of screening was defined as the age at which women with different levels of breast cancer risk attained a 10-year cumulative risk level similar to women aged 50 years in the general population.Of the 1 549 988 women enrolled in this study, 3895 had breast cancer . Participants were divided into different risk groups according to breast cancer risk scores . Using the 10-year cumulative risk of breast cancer at age 50 years in the general population as a benchmark , the optimal starting age of screening for women with high, medium, or low risk of breast cancer was identified as 43, 48, or after 55 years, respectively. An online calculator was developed to calculate an individual’s optimal starting age of screening.This study identifies the risk-adapted starting age of breast cancer screening based on the principle of equal management of equal risks, which may inform updates of current screening guidelines. Among women, breast cancer ranks first for cancer incidence and mortality in most countries.3 In theory, screening interventions should be applied to high-risk populations based on a risk-adapted screening strategy5; however, the evidence is limited worldwide.6 To our knowledge, most randomized clinical trials and guidelines for breast cancer screening have recommended a one-size-fits-all approach in which all women are advised to begin screening at age 50 years, even those with risk factors .11 A one-size-fits-all or age-oriented screening strategy does not consider individual variation in terms of breast cancer risk,12 which is not beneficial for young women at increased risk whose breast cancer incidence rate has gradually increased with distant-stage diseases in recent years.14 Therefore, a risk-adapted starting age of screening is warranted to ensure the fairness and effectiveness of breast cancer screening applications.Evidence has consistently shown that screening and early detection reduce breast cancer mortality.15 To explore the risk-adapted starting age of breast cancer screening in China, we used data from the Cancer Screening Program in Urban China (CanSPUC)—the largest nationwide multicenter community-based screening cohort—and treated 10-year cumulative risk at age 50 years as the benchmark. We hypothesized that women with different levels of breast cancer risk should be screened at a risk-adapted age.The recommended initial age for breast cancer screening in most guidelines is 50 years, which is consistent with the peak age of breast cancer diagnosis in China.STROBE) reporting guideline.This cohort study was approved by the ethics committees of the China National Cancer Center/Cancer Hospital, the Chinese Academy of Medical Sciences, and Peking Union Medical College and of all hospitals involved. All participants provided written informed consent. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology , brain (ischemic or hemorrhagic cerebrovascular disease), or lungs  were recruited through telephone calls, personal encounters, social media, and community advertisements. Participants answered a comprehensive questionnaire about their exposure to risk factors, and women with increased breast cancer risk were recommended to undergo ultrasound or mammography screening. Unscreened participants who entered the cohort from January 1, 2013, to December 31, 2018, were analyzed for this study, which covered a total of 19 provinces in China.16 smoking status (nonsmoker or smoker), drinking status (nondrinker or drinker), age at menarche (<13 or ≥13 years),9 delivery history (yes or no), breastfeeding (yes or no), benign breast disease (yes or no), and first-degree family history of breast cancer (yes or no). Smokers were defined as those who were currently smoking or had previously smoked tobacco more than once per day for at least 6 months. Drinkers were defined as those who were currently drinking or had previously consumed alcohol more than once a week for at least 6 months. Delivery history included natural labor and cesarean birth. Benign breast disease included hyperplasia of mammary glands, nodules, ductal ectasia, or benign fibroma.The baseline survey assessed the following covariates: age , marital status , body mass index ,International Statistical Classification of Diseases, Tenth Revision codes were used to identify women who developed breast cancer as the primary tumor, in which breast cancer was coded as C50.Study outcomes included breast cancer diagnosis and age at diagnosis. All cancer-related examinations and results were obtained from hospital medical records, the China National Central Cancer Registry, and the health insurance system. Women were followed up from the cohort entry date and left the study at the date of breast cancer diagnosis, death, emigration, or study end , whichever came first.t or Wilcoxon rank-sum test was used to compare differences between groups. Categorical variables are expressed as percentages, and the χ2 or Fisher exact test was used to compare differences between groups. Breast cancer risk factors were identified with Cox proportional hazards regression models and clinical significance. The multivariable Cox proportional hazards regression model including breast cancer risk factors was used to calculate the risk score for each participant. Risk division was performed according to risk score. Risk scores were divided into different risk levels according to the number of participants and the trends in 10-year cumulative risk curves.Continuous variables are expressed as the mean (SD), and the 17 The age-specific annual incidence rate equaled the number of cases for each age divided by person-years for that age. The 10-year cumulative incidence rate was calculated as the sum of each subsequent 10-year age-specific annual incidence rate at each age. Finally, 10-year cumulative risk equaled 1 – exp (– the 10-year cumulative incidence rate). The risk-adapted starting age of screening was defined as the age at which women with a particular risk of breast cancer attained a similar level of 10-year cumulative risk to the general population at age 50 years.Ten-year cumulative risks were calculated as follows.18 for internal validation of 10-year cumulative risk for different risk levels was conducted to assess the stability of the model used to calculate risk scores. Data analysis was performed from October 1, 2021, to August 16, 2022. In China, current guidelines recommend that women at average risk receive breast cancer screening starting at age 45 years, although this is based on expert opinion and lacks population-based evidence.19 Therefore, 10-year cumulative risk at age 45 years was also treated as the alternative benchmark for sensitivity analysis.A 2-fold cross-validation methodP < .05 was considered statistically significant.All analyses were conducted using R version 4.1.2 . All statistical tests were 2 sided, and P < .01) and had a higher BMI , younger age at menarche , no breastfeeding history , benign breast disease , and a first-degree family history of breast cancer . For women in the general population, the risk of developing breast cancer in the next 10 years was 2.65%  at age 50 and 2.32%  at age 45.Of the 1 731 422 women in the CanSPUC cohort, 1 549 988 were included in this study . There wAll selected risk factors  used to calculate breast cancer risk scores were significantly associated with breast cancer incidence in the multivariate Cox regression model. Hazard ratios (HRs) ranged from 0.70  to 1.27  and high-risk  groups had a greater tendency to develop breast cancer (P < .001) and similar to that for the low-risk  and medium-risk  groups  reached 10-year cumulative breast cancer risk equal to that of women with average risk at age 50 years in the general population . ComparaUsing 10-year cumulative risk at age 45 years as an alternative risk benchmark, our proposed breast cancer risk stratification demonstrated consistent breast cancer incidence trends in unscreened women in the CanSPUC cohort. As a result, risk equal to that of women at average risk at age 45 years in the general population was achieved 3 years later in the low-risk group and 1 and 3 years earlier in the medium-risk and high-risk groups, respectively .http://cancerrc.ncsis.org.cn/#/utilshome) to calculate an individual’s optimal starting age and to support decision-making in breast cancer screening.This cohort study examined the risk-adapted starting age of breast cancer screening in China by using CanSPUC population-based national cancer screening program data, with more than 1 million women eligible. Women with low breast cancer risk were proposed to start screening after age 55 years, whereas those with medium or high risk were proposed to begin screening at age 48 or 43 years, respectively. We also generated an online tool  than those without the disease. Evidence suggests that breastfeeding is inversely associated with breast cancer25 and exerts a notable protective effect against hormone receptor-negative breast cancers.26 Younger age at menarche is also associated with a higher risk of breast cancer. A meta-analysis of 117 studies showed a 5.0% reduction in breast cancer risk for every year older at menarche.9 Furthermore, the results of a meta-analysis of studies in East Asian women showed that obesity is independently associated with increased risk of breast cancer in postmenopausal women.27 Although all of these factors were considered in the China Guideline for the Screening and Early Detection of Female Breast Cancer,19 the recommended starting age of screening for women at average and increased risk was based on expert opinion and lacked population-based evidence in Chinese women. Evidence of a risk-adapted starting age of screening is needed to determine populations at high risk and promote screening of these individuals, which can further aid in efficient allocation of limited medical resources, especially in developing countries such as China.In this study, breast cancer risk factors used for risk division included first-degree family history of breast cancer, benign breast disease, breastfeeding, age at menarche, and BMI. A family history of breast cancer is a well-demonstrated risk factor28 These authors considered a family history of breast cancer, reproductive profile , and other family history of cancers separately in their study, which makes it difficult for individuals with more than 1 category of risk factors to decide when to screen.To date, studies focused on risk-adapted starting ages of breast cancer screening have been conducted with the Swedish family cancer data sets by Mukama et al.29 which also means that the population analyzed here is representative of Chinese women in the general population. Second, all cancer-related examinations and results were obtained from hospital medical records, the China National Central Cancer Registry, and the health insurance system, which contributed to study reliability. Third, we combined 5 factors associated with breast cancer and classified women into groups with different risk levels according to combined risk scores, which is more practical for clinical use. In addition, although the median follow-up time of the study was no more than 5 years, we found that the incident age of breast cancer in women in the general Chinese population was significantly older compared with women at high risk, which supports the recommendation of a risk-adapted starting age of breast cancer screening. We believe that a difference would also be found between the medium-risk or low-risk groups and the general population of women with longer follow-up. Finally, we generated an online calculator based on the proposed risk-adapted age to support individual decision-making.Several improvements were made in our study. First, we analyzed more than 1 million community-dwelling women from a multicenter, population-based, prospective cohort. Baseline characteristics in our study  were also similar to data from the 2020 China Population Census Yearbook,31 Second, although 5 factors were selected for risk division in our study according to clinical and statistical significance, more key variables associated with breast cancer  should be collected and used for risk division in future research. In addition, risk-adapted starting age in our study was defined as the age at which women with different risk levels attained a 10-year cumulative risk comparable to the average risk for women at the recommended age in the general population. The cost-effectiveness of this approach was not taken into consideration and should be assessed in further studies. In addition, external validation should also be conducted. Screening methods such as mammography34 and clinical breast examination35 are proven to be effective for breast cancer screening. Screening strategies include not only the starting age of screening but also screening methods and screening intervals, which should be investigated for risk-adapted breast cancer screening in the future to detect women at high risk for breast cancer. Doing so helps to focus limited resources on these individuals, especially in developing countries such as China.This study has some limitations. First, the age of 40 years recommended by certain guidelines was not explored due to the limitation of the starting age in this study.This cohort study reports the risk-adapted starting age of breast cancer screening in China. In addition, we developed a website to calculate an individual’s optimal starting age of screening, which may support the decision-making process and inform the update of current breast cancer screening guidelines. Our findings contribute to the principle of equal management of equal risks in breast cancer prevention and may help narrow the focus to women at high risk. Doing so may optimize the allocation of limited screening resources, especially in developing countries like China. Future research on risk-adapted screening methods and screening intervals is needed.


Very little is known about the breast cancer risk profile among American Indian women. Previous research shows that the proportion of American Indian/Alaska Native women with baseline characteristics (commonly known breast cancer risk factors) differs from other ethnicities. This retrospective case control study was designed to the explore the association of these factors among American Indian women with and without breast cancer.n = 141), two controls were selected when possible (n = 278). Risk factors examined included woman's age, age at first live birth, age of menarche, the number of previous benign breast biopsies, the total number of first-degree relatives with breast cancer, body mass index and parity. Odds ratios and 95% confidence intervals were calculated using logistic regression.Cases and controls were retrospectively selected from the medical records of American Indian women who obtained their health care from Quentin N. Burdick Memorial Health Care Facility  in Belcourt, ND. For each woman with breast cancer  in Belcourt, ND, between risk factors commonly identified in other populations and breast cancer were weakly positive. Nulliparity was the only risk factor to consistently show a positive significant association .Disparities in breast cancer incidence, mortality and screening among Northern Plains American Indian emphasize the need to better understand the risk factors associated with breast cancer in this population. Based on the results of this study, the value of current risk prediction models in American Indian communities is uncertain and clinicians should be cautious in using these models to inform American Indian patients of their risk for breast cancer. American Indian women are significantly impacted by breast cancer incidence and mortality. According to the American Indian Cancer Foundation, 1 in 8 American Indian women will get breast cancer in their lifetime . During n = 696) , limited the author's ability to make conclusions about the associations of these factors with breast cancer  . The pron = 696) . Resultsn = 696) . Howevert cancer .Historically, breast cancer risk factor data has been used to inform the creation of breast cancer risk assessment tools. A breast cancer risk calculator is a mathematical model that is commonly used by physicians to determine a woman's risk of breast cancer. Physicians also use information from these models to inform their patients about what risk factors are increasing their risk of breast cancer. Some of the more commonly known calculators are Gail, and Rosner and Colditz . Other pThis study explored risk factors included in the Gail model as well as BMI and parity for an American Indian population in North Dakota. Given that the model was adjusted for the same risk factors in other populations, it was hypothesized that the risk factors would be the same in the American Indian population; however, the magnitude of the associations would likely differ.This study was a quasi-case-control study design and was undertaken to assess the association between potential risk factors and breast cancer among a population of American Indian women who obtained their health care from Quentin N. Burdick Memorial Health Care Facility  in Belcourt, ND. The study was approved by the Turtle Mountain Band of Chippewa Indians Research Review Board, Great Plains Institutional Review Board (IRB) and the University of Minnesota IRB. The original study was completed while lead author (Nadeau) was at the University of Minnesota, hence the IRB was included from that location. A resolution in support of the study was also passed by the Turtle Mountain Band of Chippewa in Belcourt, ND.n = 1), unable to identify controls (n = 2), unable to retrieve chart from archives (n = 4), no previous mammogram (n = 9), and unable to confirm a pathological diagnosis for breast cancer (n = 13).An electronic database, the Resource and Patient Management System, at the Indian Health Service was queried using ICD-9 code 174 to retrospectively identify patients with a breast cancer diagnosis (cases) from May 1990 through January 29th, 2016. Case and control ethnicity was defined as American Indian by virtue of their eligibility for care within the Indian Health Service and was subsequently verified in each medical record. A total of 170 patients with a possible breast cancer diagnosis were identified. A pathological diagnosis was confirmed for 141 (82.94%) patients. The remaining 29 were eliminated for the following reasons: unable to find risk factor data  of the diagnostic mammogram of the case. Due to matching restrictions, there were four cases where we could only find one control. This resulted in a total of 141 cases and 278 controls with an age range of 29 to 88 years of age.Since 1986, the Quentin N. Burdick Healthcare Facility asked every woman undergoing a mammogram to complete a brief questionnaire that included the following questions about risk for breast cancer: date of birth, age at first pregnancy, menstrual history – age at onset, ever had breast surgery , and if any blood relative had breast cancer . These questionnaires, kept in the Radiology department, served as the primary source of data for the study.Each study participant was assigned a unique study ID which was linked to their chart number on a list that was kept in a secure area at the Indian Health Service facility. Data was recorded for risk factors included in the study. Date of previous benign breast biopsy, date of mastectomy and if the patient had an active radiology, electronic, and hard chart on file were also recorded. It was also noted whether review was completed for the radiology, electronic and medical chart files , age at first live birth , age of onset of menstruation , the number of previous benign breast biopsies , the number of first-degree relatives with breast cancer , BMI , number of live births .For Phase II of the analysis, the distribution of the data was assessed, and new adjusted categories were proposed. Compared to the general population, American Indian women in this study were screened at an older age and were younger at age of first live birth , age at first live birth , age of onset of menstruation , the number of previous benign breast biopsies , the number of first-degree relatives with breast cancer , BMI , number of live births .The following statistical models were computed for both Phase I  and Phase II (adjusted categories) of the analyses, (1) logistic regression using the Gail model , (2) logistic regression of the Gail model with BMI added, and (3) logistic regression of the Gail model with both BMI and parity added (first live birth was removed to avoid multicollinearity complications with parity). Odds ratios (OR) along with 95% confidence intervals (CIs) were reported. The c-statistic was used to test the model as a whole.Medical record review occurred retrospectively for 141 cases and 278 controls matched by age of women and date of breast cancer screening at Quentin N. Burdick Memorial Health Care Facility  in Belcourt, ND. Five variables from the Gail model for predicting risk of breast cancer were used in this study . Table 2A in When multivariate analysis, restricted to the four risk factors included in the Gail model, was conducted, women who had never given birth were over twice as likely to have had breast cancer compared to women whose age at first live birth was before age 20 , age at first live birth , age of onset of menstruation , the number of previous benign breast biopsies , the number of first-degree relatives with breast cancer , BMI , number of live births . When the risks based on adjusted categories were analyzed, women who had an age at onset of menstruation of 12 was significant  in Belcourt, ND. The risk factors that were expected to be positively associated with breast cancer, for the most part, showed a null and/or an inverse relationship. The c-statistic for all models in Phase I and Phase II of the analysis ranged from 0.59 to 0.63. This statistic indicates poor model discrimination which means that the models do not contain variables that are strongly associated with the outcome of breast cancer. If the regression models contained explanatory variables that were strongly associated with the outcome, improved discrimination would be expected (Risk factors for American Indian women in the Great Plains region may differ and/or vary in magnitude compared to those identified in other populations. Additional breast cancer risk factors have been identified over the past two decades but have not been included in any models . These rIt is unlikely that a single Breast Cancer Risk Assessment Tool could be developed that would work for all tribes or regions of American Indian women. A total of 574 tribes are federally recognized across the United States, each one differing in their culture, environment, healthcare, and behavioral health, some of them dramatically (For the study, the availability of medical records data for potential breast cancer variables was limited. Variables previously associated with breast cancer, such as smoking and breast feeding, were unavailable. Further studies may have improved models if they include these variables, reducing variance in the model. Our sample was limited to women who had a mammogram. This may have biased the data if women are only likely to have a mammogram if they have a problem or can easily obtain the procedure. Further, the matching between cases and controls was not exact nor complete. Our conclusions could have been strengthened if the control group was enlarged, or if more precise matching had been available.Various studies have stratified data by looking at risk factors specific to premenopausal women and/or postmenopausal women –10. SeveTools to help gauge the risk of breast cancer for American Indian communities are non-existent. The creation of validated models could potentially result in data driven estimates in an easy-to-use format and would be useful for studying which factors increase the risk of breast cancer among American Indian women. Existing data for white women is currently being used to inform specific clinical decisions, plan intervention trials and counsel American Indian women about their risk of the disease because data on American Indian women is limited .Conducting this study was an important first step in gaining a better understanding of the breast cancer risk factors among American Indian women in Belcourt, ND. The results support the need to explore additional potential breast cancer risk factors in this population, such as oral contraceptive use, alcohol drinking, active smoking, obesity, diabetes mellitus, menopausal status, and breastfeeding. Since so little is known about the breast cancer risk profile for American Indian women, future studies are needed for other American Indian tribes. Population-based studies with an ample sample size to allow for stratified analyses while exploring multiple breast cancer risk factors in other geographical regions with a substantial American Indian population are needed.It is important to have data driven, population-specific breast cancer risk estimates so clinicians are better able to predict breast cancer risk at the individual and population levels. Disparities in breast cancer incidence, mortality and screening among American Indians emphasize the need to better understand the risk factors associated with breast cancer in this population. Once breast cancer risk factors are identified, appropriate interventions can be designed and implemented in order to reduce the breast cancer burden in American Indian communities.The original contributions presented in the study are included in the article/MN contributed to the conception, design of the study, and wrote the first draft of the manuscript. MK, LB, and KW wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.This research was supported in part by the University of Minnesota J. B. Hawley Student Research Award and the National Institute of General Medical Sciences of the National Institutes of Health under Award Number U54GM128729.The content is solely the responsibility of the authors and does not necessarily represent the official views of the University of Minnesota, University of North Dakota, or the National Institutes of Health.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Little is known about the role of economic recessions in the risk of cancer. Therefore, we evaluated the impact of the severe economic recession in Finland from 1991–1994 on the incidence of all cancers and cancer subtypes among a middle-age and older population.From the Kuopio Ischemic Heart Disease Risk Factor Study (KIHD), a population-based sample of 1,620 women and men aged 53–73 years were examined from 1998–2001. The cancer-free participants completed a questionnaire on the possible impact of the 1990s recession in Finland on their lives. Incident cases of cancer were obtained through record linkage with the Finnish Cancer Registry. Cox proportional hazards regression was used to estimate hazard ratios (HR) of incident cancer events after adjusting for possible confounders.P = 0.05). Prostate-genital cancer was 71% higher among men with hardships . No association was observed between socioeconomic hardships and subsequent risk of total or any subtype of cancer among women.A total of 1,096 cancer-free participants had experienced socioeconomic hardships due to the recession at the baseline. During 20 years of follow-up, 473 participants developed cancer. After adjustment for age, baseline socioeconomic position, and lifestyle factors, the risk of all cancers was 32% higher among men who experienced socioeconomic hardships compared to those who did not (HR 1.32; 95% confidence interval [CI], 1.00–1.74, The 1990s economic recession was associated with increased risk of all cancers, especially prostate-genital cancer among Finnish middle-age and older men, but no association with cancer was observed in women. Socioeconomic position (SEP) and the change of SEP across the life-course are likely to influence cancer risk factors, thus associating with cancer incidence and mortality.2 Economic recessions often change the SEP in part of the population,3 causing various social and financial disadvantages, which are termed as socioeconomic hardships.4Cancer is the second leading cause of death worldwide.5,6 The global financial crisis in 2008, particularly, awakened the scientific community to the possible effects that recessions may have on cancer. Research findings, however, are still inconsistent.5,7 Some studies have found an increase in cancer mortality among men and women since the onset of recession.8–13 Some, in contrast, have reported a decline in cancer mortality14 and lower cancer incidence during recession.15,16 To our best knowledge, there are no prior studies examining possible longer-term impacts of recessions on subsequent cancer incidence.Most of the previous studies on recessions and health have investigated the physical and mental health only among those who have become unemployed during recessions.3 This economic downturn affected the country for many years after and caused unemployment rates to peak at 19.8% in 1996, while it had been 5.2% prior to the recession in 1989.17 Although the dramatic changes in the Finnish economy likely affected the population health in many ways, few studies have investigated the details, and most studies have focused only on all-cause mortality during the recession period.17,18 Therefore, we wanted to investigate whether the socioeconomic hardships that resulted from the 1990s severe recession would suggest longer-term impacts on the Finnish population health. Specifically, the study aimed to examine the post-recession incidence of cancer in a population-based sample of middle-age and older women and men in Eastern Finland by comparing those who had and those who had not been exposed to socioeconomic hardships during the recession.Finland experienced a sudden, rapid, and exceptionally severe collapse of its economy in the early 1990s following many years of strong economic growth during the 1980s.19 KIHD is an ongoing prospective population-based study, which initially started in 1984 to investigate the different risk factors of cardiovascular disease (CVD), atherosclerosis, and related outcomes in middle aged men in Eastern Finland. Later, it extended to study other non-communicable diseases. The first cohort consisted of 1,166 men who were 54 years old, enrolled in 1984–1986. To extend the study to cover more age groups, additional sampling and baseline examination was performed in 1986–1989, and it included groups of 42-, 48-, and 60-year old men, in addition to original 54-year olds . A total of 920 women aged 53–73 years participated in KIHD for the first time from 1998–2001.20We performed a prospective analysis among the participants from the Kuopio Ischemic Heart Disease (KIHD) Risk Factor Study.Our study is based on 1,774 middle-age and older women and men who were examined from 1998–2001. The women cohort comprised of 920 women  aged 53–73 years. The men cohort comprised 854 men aged 53–73 (85.6% of those who participated in KIHD from 1986–1989)  as well as participants with a prior history of cancer (n = 130). After the exclusions, 1,620 women and men who were free of cancer were included in the study sample  was calculated by dividing weight in kilograms by the square of height in meters.23A questionnaire was used to check smoking status.24 Our study cohort was linked with the FCR data using the 11-digit personal identity code mandatory to every resident of Finland. Outcome was assessed annually through re-linkage with the registry using these personal identity codes. All cancer events that occurred between the baseline examination and the end of 2017 were included.Incident cancer cases were derived from the Finnish Cancer Registry (FCR), which is a national population-based digital registry covering all of Finland with no lost cancer cases since 1953. The cancer diagnoses for the registry are determined and reported by the secondary or tertiary health care units .2 independency test for categorical variables to explore bivariate relationships.The univariate associations between experiencing socioeconomic hardships and baseline socioeconomic, lifestyle, and clinical characteristics were assessed using means and linear regression for continuous variables and ChiNo wash-out period for incident cancer cases was necessary, since the economic recession had already occurred in Finland in the early 1990s; in other words, more than 5 years before the study subjects participated in the study and the cancer follow-up started.Hazards ratios (HRs) for the risk of cancer according to socioeconomic hardships exposure binaries were estimated using Cox regression models. The analysis was performed on three different levels: first, by investigating the HRs for the risk of all cancer events among all participants, then by stratifying according to gender, and finally, by investigating the HRs for the risk of each cancer subtype among each gender. The category that did not experience any hardships was considered as the reference. The criteria for selecting confounders were based on established risk factors for cancer or on associations with exposures or outcomes in the present analysis.2).Two models were used to adjust for potential confounders in the prospective analyses. Model 1 adjusted for age (years). Model 2 additionally adjusted for sociodemographic variables of education (years) and marital status , smoking status (yes/no), alcohol intake (g/week), physical activity (hour/year), mean of fruits, vegetables and berries consumption (g/4 days) and BMI . All analyses were conducted with the SPSS statistical software .Missing values within each of the covariates (<0.5%) were replaced by the cohort mean. All A total of 1,096 (68%) women and men reported experiencing socioeconomic hardships during the 1990s recession. Baseline characteristics of the participants are presented in Table P ≤ 0.001). Men who experienced hardships had less education  compared to men who did not  (P = 0.03). In women, there were no significant differences in the educational level between exposed and non-exposed.The mean age for participants with socioeconomic hardships was 61.9  years compared to 64.1  years of those who had no hardships (P = 0.07). Further adjustments for baseline SEP and lifestyle variables slightly strengthened the association . When stratified by gender, the increased risk of any type of cancer was observed only among men who had experienced socioeconomic hardships . However, the P-value for gender interaction was 0.45, actually not suggesting clear gender-based interaction.During mean follow-up of 20 years, the total number of new cancer cases observed in the cohort was 473, of which 216 were among women and 257 among men. After adjustment for age (model 1), the risk of any cancer was 20% higher among participants (men and women combined), who experienced socioeconomic hardships compared to those who did not   pandemic will trigger numerous studies on the economic crisis and its impact on population health,31 new viewpoints are hopefully opened. To put it shortly, more research is still needed to better understand the role of macroeconomics on health and on disease etiology. It can be already agreed that health policies should give more attention to preparatory investments and proactive measures in public health programs, education, counselling and other human resource-based health activities.Harmful health effects of recessions might take several years to become evident. Therefore, longer-term follow-ups are needed in epidemiological studies.Our epidemiological study is based on a regionally and ethnically representative population-based sample. The follow-up time of almost 20 years can be considered sufficiently long in these age groups. The comprehensive and reliable nationwide system of digital registers that was utilized in our study, covers all data on hospital discharge diagnoses, causes of death, and incident cases of cancer in Finland. Therefore, the outcome measure in our follow-up study can be considered reliable.The majority of previous studies on recessions and cancer have used unemployment as the only measure of socioeconomic hardships. Instead, we used a detailed questionnaire to draw a broader estimate on how the participants were overall affected by the recession, including, but not limited to, unemployment. Finally, the large dataset of the KIHD study allowed for a broad range of well-validated measures to adjust for in our models predicting incident cancer. Most previous studies have focused on cancer mortality only, with much more limited set of covariates to control for.Since our study was based on an ethnically homogenic population of middle-aged and older Finnish women and men, we cannot necessarily generalize the results to other ethnic groups and countries. On the other hand, at least the other Nordic countries share fairly similar demographic characteristics and same type of social and welfare system. Still, the results may not be generalized even in Finland to women and men other age groups than to those we studied.To avoid over-adjustment, we included only two covariates describing socioeconomic position (SEP); namely, education and marital status. This was justified simply by the fact that the other widely used SEP components, income and occupation, were already included in the participants’ responses on whether they were affected by the recession or not. The size of our cohort was large enough to allow only analysis of the most common cancers, therefore some potentially important associations may have been missed. Finally, while our study showed an association between socioeconomic hardships and subsequent cancer risk, any causal inferences regarding cancer pathophysiology should be treated with extreme caution.Our study suggests that economic recessions may pose gender-specific cancer risks to middle-age and older Finnish men, but not necessarily to women. The 1990s severe economic recession in Finland and the subsequent socioeconomic hardships it imposed on men were associated with an increased risk of developing especially prostate-genital cancer during the 20 years of follow-up.


Evidence on the association between mental health disorders and cancer risk is inconclusive, despite well-established associations between mental health disorders and lifestyle factors such as smoking. This study examines the relationships between depression, anxiety and cancer risk, and the potential mediating effects of lifestyle factors.A study of 34,571 participants aged 40–69 years in the CARTaGENE cohort was conducted. Depression was defined by questionnaire (PHQ-9), antidepressant use, and a composite of questionnaire, antidepressant use, or lifetime self-reported physician diagnosis. Anxiety was defined by questionnaire (GAD-7). Co-morbid depression and anxiety was also assessed. Cox regression models were used to investigate associations between mental health and risk of prostate, lung, and all cancers combined. Mediating effects of lifestyle factors were assessed using Baron and Kenny mediation criteria.There were positive associations between mental health disorders, all cancers and lung cancer risk, however with the exception of anxiety and lung cancer in women , associations were attenuated with adjustment for sociodemographics, health status and lifestyle factors. In the mediation analysis, smoking accounted for 27%, 18%, and 26%, of the total effect between depression (PHQ-9), anxiety, and co-morbidity and lung cancer, respectively in women. In men, smoking accounted for 17% of the total effect between depression  and all cancers.Positive associations were observed between mental health disorders, all cancer and lung cancer risk, however most relationships were attenuated with adjustment for lifestyle factors. Smoking status mediated a significant proportion of the relationships between mental health disorders and cancer risk. It is estimated that approximately 1 in 2 Canadians will develop cancer in their lifetime, and 1 in 4 Canadians will die from cancer [Mental health disorders also have a high public health burden. It is estimated that 1 in 5 Canadians are affected annually by mental health disorders . AccordiLifestyle factors are closely associated with anxiety, depression, and cancer. Individuals with depression and anxiety are more likely to smoke, consume higher levels of alcohol, engage in lower levels of physical activity, have lower diet quality, and experience disturbed sleep compared to those without depression or anxiety –18. ThesGiven the established association between healthy lifestyle behaviours with cancer, and mental health disorders, it is possible that the inconsistent associations between depression, anxiety and cancer risk are due to lack of consideration of the impact of behavioural factors. It may also be pertinent to consider the potential modifying role of sex on relationships. There are well established differences in the prevalence of mental health disorders, symptoms and usage of mental health services , 22. TheParticipants for the present study were drawn from the CARTaGENE cohort, a study of 43,000 men and women aged 40–69 in Quebec, Canada. Participants were enrolled between 2009 and 2015 as part of the national Canadian Partnership for Tomorrow’s Health (CanPath), a prospective, longitudinal cohort study . DetailsAt study baseline participants completed self-report questionnaires that asked about sociodemographic factors, medications, and health and lifestyle factors . A propoParticipants were excluded if they had any incomplete information from the anxiety (GAD-7) and depression (PHQ-9) questionnaires , were missing date of cancer diagnosis (n = 34), or reported a prior cancer diagnosis . Participants who were diagnosed with cancer within the first six months of follow-up (n = 236) were also excluded to minimize reverse causation. After exclusion criteria was applied, the final analytical sample size was 34,571 participants. This included 2,888 total incident cancer cases, with 329 prostate and 305 lung cancer cases.Depression at baseline was assessed using multiple definitions that capture different time frames and severity of depression. Depressive symptoms at baseline were assessed using the validated PHQ-9, a nine-item questionnaire that assesses symptoms of depression over the past two weeks . A scoreSelf-reported depression data was also collected in the baseline questionnaire. Participants were asked “has a physician ever told you that you have depression?” Positive responses to this question were recorded as a lifetime prevalence of depression. Self-reported depression was considered in a composite measure of depression which was a positive response for any of PHQ-9 score ≥10, use of antidepressants, or self-report of physician diagnosis.Anxiety symptoms were assessed at baseline using the validated GAD-7, a seven-item self-report scale that assesses symptoms over the past two weeks . A scoreCo-morbid depression and anxiety was defined as participants who scored a 10 or higher on both the PHQ-9 and the GAD-7 questionnaires.Covariates assessed for potential confounding and mediating effects were selected based on previous research and included sociodemographic factors , lifestyle factors , fruit and vegetable consumption (servings/day), and health status , and without partners . Household income was categorized as less than $50,000, $50,000–74,999, $75,000–150,000, and greater than $150,000.2 as underweight/normal, 25.0 to 29.9 kg/m2 as overweight, and ≥ 30 kg/m2 as obese. BMI from measured height/weight was used when available, otherwise self-reported height/weight was used. Servings of fruits and vegetables per day were dichotomized into: <five or ≥ five in line with recommendations for consumption to support overall health [Current smokers were those who had smoked at least 100 cigarettes in their lifetime and smoked in the past 30 days. If participants had smoked at least 100 cigarettes in their lifetime, but not in the past 30 days, they were categorized as past smokers. All other participants were categorized as non-smokers. Participants were classified as abstainers , former , occasional (≤2–3 times/month), regular (≥ once/week but ≤2–3 times/week), and habitual drinkers (≥4–5 times/week). Average hours of sleep were categorized as <7 hours, 7–9 hours, and 9+ hours per night . Physical health . Self-re‘Multivariate Imputations by Chained Equation (MICE)’ package in R [Missing covariate data was imputed with the age in R . The pro‘survival’ package in R [Cox proportional hazards regression analysis was used to estimate hazard ratios (HR) and 95% confidence intervals (CIs) between the exposure and outcome variables. The proportional hazards assumption was verified using Schoenfeld residuals with the age in R . Days ofage in R . Days ofmediation package in R software [In order to clarify the role of lifestyle factors on the causal pathway between mental health disorders and cancer risk, a mediation analysis was performed to calculate the direct, indirect, and total effect using the software . First, software , 47. Forsoftware . The presoftware . A p-valTable 1).A greater prevalence of high PHQ-9 scores, meeting thresholds for depression were observed in female participants, participants with an annual household income of less than $50,000, participants with a high school education or lower, participants who were single, divorced, or widowed, participants with diabetes, and participants with COPD (Table 2).Similar to the comparison of depression status, greater proportions of GAD-7 scores that met thresholds for anxiety were observed in female participants, participants with an annual household income of less than $50,000, participants with a high school education or lower, participants who were single, divorced, or widowed, participants with type II diabetes, and participants with COPD . Anxiety defined by GAD-7 scores occurred in 4.6% of the sample, and comorbid anxiety and depression was observed in 2.7%.Mental health characteristics of the analytic sample are presented in Table 3). The proportion of women with a GAD-7 score over the threshold of 10 points indicating anxiety (5.4%) was also higher than men (3.6%) (Table 3). This disparity was also reflected in the proportion of women with depression , the proportion of women diagnosed with depression by a physician in their lifetime , proportion of women using antidepressants , and proportion of women with comorbid depression and anxiety  (Table 3).Women had a higher mean GAD-7 score (2.8) and PHQ-9 score (3.2) compared to men  (Table 4). Similarly, no associations were observed between PHQ-9 scores on a continuous scale and risk of cancer  follow-up time for all cancers was 3.5 years  for men and 3.4 years  for women. Among all three depression definitions, the risk estimates for men and women tended to be above 1.0, although, generally associations were statistically non-significant . Similar). Associations between anxiety, and comorbid anxiety and depression with prostate cancer are not presented due to low events , but were similarly null.None of the conceptualizations of depression, were associated with prostate cancer risk . Comorbid anxiety and depression were also associated with increased risk of lung cancer among women in Model 1; HR: 2.32 (95% CI: 1.28–4.20) and Model 2: HR: 2.11 (1.16–3.85). Additional adjustment for lifestyle variables in Model 3 attenuated these associations. Anxiety was associated with increased risk of lung cancer in women (Table 6). Associations remained with full adjustment for confounders .Depression—assessed by PHQ-9—was associated with an increased risk of lung cancer among women in Model 1: HR: 1,88 (95% CI 1.15–3.09), and Model 2: HR: 1.75  , anxiety, and co-morbid depression and anxiety on lung cancer in women by a proportion of 27%, 18%, and 26%, respectively. No interactions between exposures  and mediators were observed among women, with the exception of smoking status.The depression- or anxiety-cancer associations with a statistically significant direct effect are shown in Mediation effects for men were examined for only depression and all cancer as this was the only relationship that was statistically significant. Smoking status was shown to partially mediate the relationship between depression  on all cancer in men by a proportion of 17%. No interactions between depression and any mediator were observed among men.Findings from this large study of Canadians from Quebec suggest that there may be a modest positive association between mental health disorders and risk of all cancers and lung cancer. These relationships persisted with adjustment for sociodemographic and health variables but were generally attenuated with adjustment for lifestyle factors, showing both the importance of adjustment for these factors in future studies and further underscoring the importance of healthy lifestyles for cancer prevention. Further, smoking may be an important and modifiable health behaviour that contributes in part to relationships between depression (PHQ-9), anxiety (GAD-7), and co-morbid depression and anxiety and lung cancer risk in women. Similarly in men, findings suggest that of the lifestyle variables explored in our study, smoking may be the largest contributor to relationships between depression  and all cancer incidence.Evidence on the relationship between mental health disorders and cancer risk is inconsistent; with some studies reporting significant positive relationships for some cancer subtypes but not others, and other studies presenting non-significant findings , 50, 51.The present study primarily focussed on mental health exposures as presence or absence which may not capture the range of symptoms or specific aspects of mental health that are relevant for cancer. A cohort study in Korea found inconsistent results within their research on mental health disorders and prostate cancer risk. In a dose-response analysis, mild depression was significantly associated with subsequent prostate cancer risk, while moderate and severe depression were not . InteresThe results of our mediation analysis are consistent with the findings of Trudel-Fitzgerald et al., who found that lifetime pack-years of smoking mediated 38% of the overall association between depressive symptoms and lung cancer risk in women . Since sA strength of this study was the large cohort size that included men and women reporting a wide range of income and education, which increases the generalizability of the findings . A furthLimitations of this study include the short overall follow-up time, which given the long latency of most cancers may not have accurately captured the association between mental health disorders and cancer incidence. The short follow-up was also reflected in a modest number of incident cancers. This may have obscured more modest associations between mental health and cancer risk in the analyses of cancer sites and further stratification by sex. Although individuals with an incident cancer in the first six months of follow-up were excluded, it is possible that participants had asymptomatic or early stages of cancer that were not captured at baseline. Another limitation was the limited availability on incidence of cancer sites beyond prostate and lung cancer due to restrictions on data availability at the time of our analysis. Mental health variables and lifestyle factors were measured simultaneously at baseline within the CARTaGENE cohort. Assessment of exposure and mediator variables at the same time point weakens the argument for causation in the mediation analysis, which limits the robustness of findings from this part of the study . CautionAdditional studies which consider cancers that are strongly associated with lifestyle factors such as breast and colorectal cancer , 58 may This study demonstrated a modest positive association between mental health disorders and lung cancer risk in women, however only anxiety and lung cancer in women remained significant with full adjustment of sociodemographic, health and lifestyle factors. The mediation analysis found that smoking may contribute to associations between anxiety, co-morbid depression and anxiety, and depression with incident lung cancer in women, and associations between depression with all cancers in men. This provides further emphasizes the importance of ongoing public health efforts to reduce smoking.S1 File(DOCX)Click here for additional data file.


As rates of total mastectomy rise, the relationships between surgery modality with domains of health-related quality of life is not well understood. This study reports differences in depression, anxiety, pain, and health status among a cohort of women scheduled to receive total mastectomy or breast-conserving surgery. Patient-reported outcomes measured preoperative differences between patients receiving total mastectomy or breast-conserving surgery in a cross-sectional design. Regression analyses was used to model health outcomes and adjust for patient demographics on patient measures. Participants scheduled for total mastectomy were more likely to report more severe symptoms of depression and anxiety. This association was non-significant after adjusting for demographic differences. Younger participants were more likely to be scheduled for total mastectomy. Age was negatively associated with symptoms of depression and anxiety. Screening patients for mental health symptoms may be particularly important among younger patients who were more likely to report depression and anxiety before their surgery and were more likely to receive total mastectomy. The impact of breast cancer on mental health is substantial, and psychological distress may affect women in all stages of their treatment, with long-lasting effects . TreatmeThe choice between total mastectomy (TM) and breast conserving surgery (BCS) is based on a variety of patient and provider factors, including size of lesion to size of breast ratio, surgeon recommendation, cancer type, and patient preference . BCS hasDespite strong evidence that both BCS and TM have similar survival outcomes for women, TM is chosen by many women due to the belief that is the safer treatment . The typUnderstanding factors leading patients to opt for TM when BCS is feasible creates an opportunity to increase rates of a procedure that has fewer surgical and psychosocial complications. Although some studies have tried to address this complex issue, many focus on shared decision-making processes , leavingSeveral studies have assessed patients’ health symptoms and satisfaction with breasts post-treatment through patient-reported outcome measures (PROMs); however, most studies assess patients after surgery or focus on a particular surgical approach. There is a paucity of literature evaluating preoperative health-related quality-of-life (HRQoL) more broadly. Understanding women’s preoperative health may help healthcare providers screen and assess those who may be at risk for worse postoperative outcomes, as other studies have down that patients reporting worse symptoms preoperatively are more likely to suffer worse pain, nausea, and fatigue after their breast cancer surgery .To characterize women’s physical and mental health prior to operative treatment, the objective of this study is to describe aspects of women’s health-related quality of life (HRQoL) before surgery for breast cancer and compare differences in medical comorbidities and mental health symptoms between women scheduled for TM or BCS. This study will also explore associations between age, comorbidities and socioeconomic status and ethnocultural differences, factors which have been previously associated with depressive symptoms ,11, incrThis study is based on a prospectively recruited sample of consecutive patients registered for BCS or TM for the treatment of breast cancer at a single academic teaching hospital site between September 2017 and August 2020. Patients referred for treatment of breast cancer are seen in consultation by one of the six breast cancer surgeons at our center where a decision to proceed with BCS or TM is made, and they are placed on the surgical wait list. Breast reconstruction is discussed with all patients proceeding with TM and interested patients are referred to plastic surgery to discuss and arrange immediate reconstruction.As an element of ongoing quality improvement initiative in the setting’s health system focusing on perioperative health, all operative patients are invited to complete a survey package. The survey package includes PROMs that measure health status, depression, anxiety, pain and symptoms. BCS and TM patients are also asked to complete the Breast-Q.A list of patients newly scheduled for BCS or TM for the treatment of breast cancer was provided to the study team. To be eligible, patients had to complete a survey package, be 18 years of age or older, not reside in a conjugate living setting such as a nursing home and be able to communicate in English.All prospective patients were contacted by phone by the study team. Participants were offered a choice of receiving the survey package through the mail or completing the survey online through a secure website. The survey package included several Patient Reported Outcome (PRO) instruments. Participants’ PROs data was linked with their medical records to accurately identify participants scheduled for mastectomy or breast-conserving surgery.The PHQ-9 is a nine-item instrument that measures depression-related symptoms and functional impairment . Each itThe GAD-7 is a seven-item instrument that measures respondents’ symptoms of anxiety ,18. EachThe PEG is a three-item instrument that measures pain intensity (one item) and pain interference (two items) . Each itThe EQ-5D-5L is a measure of health status . The EQ-To measure socioeconomic status (SES), the patient’s address was linked with Statistics Canada’s neighborhood-level measures of deprivation and marginalization, the Canadian Index of Multiple Deprivation . The CanTM [The Breast-QTM ,23 is a The demographic characteristics of participants was summarized by counts and percentages, presented for the overall sample and by surgery type. Age was categorized for presentation purposes. A chi-square test was used to test for statistically significant differences in the distribution of participants’ age categories between mastectomy or breast-conserving surgery.TM were summarized for the sample of participants and by surgery type. An analysis of variance was used to measure whether there was a statistically significant difference between mean values of PROs between mastectomy and breast-conserving surgery. As this was an exploratory study of preoperative health and treatment modality, no formal hypotheses were tested.Summary statistics of the PHQ-9 (depression), GAD-7 (anxiety), PEG (pain), EQ-5D VAS  and Breast-QThe count and proportion of participants whose PHQ-9 and GAD-7 values met or exceeded the scales’ treatment thresholds of 10 or 15 were reported for the overall sample and then stratified for mastectomy or breast-conserving surgery. Chi-square tests were used to test for statistically significant differences in the distribution of participants meeting or exceeding the treatment thresholds of 10 or 15 of the PHQ-9 and GAD-7 by surgery type.Linear regression models were used to measure associations between participant’s PROs and mastectomy or breast-conserving surgery. Each PRO  were modelled separately. Each model adjusted for participants’ age, and SES variables. A dichotomous variable was included in the models for mastectomy or breast-conserving surgery. All terms were included in the model, irrespective of their statistical significance. Linear assumptions were checked using residual and Q-Q plots.A subgroup analysis was conducted on participants scheduled for TM. Participants scheduled for mastectomy were stratified into those scheduled for immediate breast reconstruction and those not having immediate breast reconstruction. Differences in PRO values were tested between the two subgroups using a one-way analysis of variance.p-values < 0.05 were considered significant, and all tests were two-sided. All analyses were conducted with SAS. This study was approved by the University of British Columbia’s Behavioral Research Ethics Board.All p < 0.01; not shown,) though no differences in comorbidities or other factors were identified between participants and non-participants.The participation rate among eligible patients was 34%; there were 2091 eligible patients and 671 completed their PROMs. Non-participants were on average 2 years younger than participants  Among participants scheduled for TM, the most common age category was 40 to 49 years of age. Participants who report more medical and psychiatric comorbidities were much more likely to have TM, 39% (90/228) versus 19% (85/443) (p < 0.01).In the study group, 443 participants were scheduled to receive BCS and 228 participants were scheduled for TM. Of the planned total mastectomies, 122 were scheduled for immediate reconstruction. As shown in p-value = 0.03 and 0.01, respectively.) There were no statistically significant differences in mean PEG (pain) or EQ-5D VAS  values between TM or BCS.p-value = 0.04). Over 8% (75/671) of participants reported at least moderate symptoms of anxiety. Severe anxiety was more common among participants scheduled for TM .As shown in p < 0.01), as younger participants tended to have higher values on both PROs .The regression analyses of TM measure. Similar to results from the PHQ-9, GAD-7 and EQ5D BCS patients report better psychosocial and physical well-being than TM patients and also report better preoperative satisfaction with their breasts.p < 0.01) and had fewer comorbidities (p = 0.01) than those not having immediate breast reconstruction. As shown in The subgroup analysis of participants scheduled for immediate breast reconstruction and those not having immediate breast reconstruction found that participants having immediate reconstruction were younger (Our study found that women scheduled to receive breast-conserving surgery reported less symptoms of anxiety and depression than patients scheduled for mastectomy, consistent with other findings ,24,25,26This study was designed as a prospective cohort of consecutive patients scheduled for breast cancer surgery. The goal of this initial study was to explore preoperative HRQoL since there is a paucity of current literature in this population. Although the use of PROs has been increasing since the mid 2000s, different measures are used which makes it difficult to compare results. Efforts are currently underway to describe an ideal set of measures for use in breast cancer treatment . Our insAlthough our study found no difference between planned surgery type and preoperative HRQoL after adjusting for age, studies focusing on postoperative HRQoL have shown that women report better health after BCS. Jay et al.  found thSimilar to our findings, studies that have looked at preoperative patients have shown that women scheduled to receive breast cancer treatment report poor sleep quality, symptoms of depression, anxiety, fatigue and pain ,28,29. MBuiles Ramirez  used theStudies investigating contralateral prophylactic mastectomy have compared preoperative and postoperative PROMs. Lim et al., using the Breast-Q, found that when comparing patients receiving BCS to unilateral and bilateral mastectomy, there was no difference in preoperative breast satisfaction, psychosocial well-being, but a difference in both physical and sexual well-being . Parker Many studies have shown the psychosocial benefits to having immediate breast reconstruction with mastectomy ,33 howevThis study’s observation between younger age and worse mental health could be observed for a variety of reasons. Due to sociocultural norms, breasts are regarded as a symbol of femininity . For manUnderstanding the decision to pursue TM when BCS is feasible is complicated, yet important. Why women choose TM when they are safe candidates for BCS is unclear. This is a particularly relevant issue to patients, providers and researchers, as TM rates have been rising ,41,42,43It is important to acknowledge the limitations of this study. Participation bias is possible, as our analysis indicated that patients that did not participate were 2 years younger on average than participants. Although our response rate was 34%, this rate falls within the range of expected participation of studies of this design. Additionally, this was a cohort study of operative patients, so there may have been heterogeneity in the sample since participation was not limited to stages of disease or treatment. The goal of this study was to understand baseline HRQoL; tumor-related factors and impact on preoperative health, as well as understanding changes in health after surgery, will be analyzed in future studies. Additionally, the majority of data was collected before the COVID-19 pandemic, and pandemic-related symptoms of depression and anxiety were unlikely to have impacted the study. Generalizability to patients in other geographic areas may be difficult, as our patient population was comprised of residents of one province in Canada.The strengths of this study include a robust sample size, and a population of participants that are demographically diverse. Additionally, our study is the first to look at the association between surgical procedure and preoperative health in this way, leaving room for additional research in this area to identify causal pathways. These findings have suggested that younger patients have higher rates of depression and anxiety and we are working on refining our care pathways to screen for symptoms and offer referral to mental health supports if such symptoms are identified.Younger breast cancer participants were more likely to report symptoms of depression and anxiety before breast cancer surgery. Screening and preoperative referral to mental health providers may offer an opportunity to enhance perioperative care.


Increase in waist/height ratio from age 20 to 5 years before diagnosis was also highly significantly associated with risk . The associations with increases in BMI and waist/height ratio were significant independently of each other and of BMI or waist/height ratio at the start of the period of change analysed, and effects were similar for invasive and in situ tumours separately. Increases in BMI and abdominal obesity are each risk factors for breast cancer in men, independently of obesity per se. These associations might relate to increasing oestrogen levels with weight gain, but this needs investigation.Breast cancer is uncommon in men and knowledge about its causation limited. Obesity is a risk factor but there has been no investigation of whether weight change is an independent risk factor, as it is in women. In a national case‐control study, 1998 men with breast cancer incident in England and Wales during 2005 to 2017 and 1597 male controls were interviewed about risk factors for breast cancer including anthropometric factors at several ages. Relative risks of breast cancer in relation to changes in body mass index (BMI) and waist/height ratios at these ages were obtained by logistic regression modelling. There were significant trends of increasing breast cancer risk with increase in BMI from age 20 to 40  1.05‐1.17] per 2 kg/m What's new?Although obesity is an established risk factor for breast cancer in men, there have been no investigations of whether weight change is an independent risk factor, as it is in women. In an interview case‐control study including 1998 cases and 1597 controls, authors found that increase in body mass index and in abdominal obesity are each significant risk factors for breast cancer in men, independent of obesity per se. BMIbody mass indexCIconfidence intervalERoestrogen receptorHER‐2human epidermal growth factor receptor‐2IGF‐1insulin‐like growth factor‐1ORodds ratio1Breast cancer occurs infrequently in men, about 1% of the incidence rate in women. Knowledge about its aetiology is sparse and has been investigated in relatively small studies with limited variables.In postmenopausal women, the female age group most hormonally similar to men, increase in weight has been shown to be a risk factor for breast cancer,We have carried out a national interview‐based case‐control study of breast cancers incident in men in England and Wales since 2005, a far larger investigation than any previously. Here we analyse data from our study for risk of breast cancer, and subdivisions of this cancer, in relation to changes in both overall obesity and abdominal obesity.2We conducted a population‐based case‐control study in which the potential cases were male residents of England and Wales diagnosed with breast cancer, invasive or in situ, in these countries during 1 January 2005 to 31 August 2017 at ages <80 years. These cases were identified comprehensively from population‐based cancer registries providing nationwide coverage and by notification to us by clinicians.The study controls were derived from two sources, which enabled comparison between them to assess potential bias. One source was male non‐blood relatives of the cases—we asked each case whether we could approach their close non‐blood male relatives, and if so we selected one (or more) to interview based on approximate frequency matching (not pair matching) in strata by age and geographic region (in practice 1998 cases and 1597 controls). The other source was ‘Generations Study controls’—husbands of women taking part in the Generations cohort study,The potential cases were approached via their consultants to take part, and potential controls by mail. If they agreed, they were then visited by a trained research nurse who interviewed them using a structured questionnaire and took a blood  sample for genetic analyses. The questionnaire asked about a wide range of demographic and potential risk factors, including height at age 20 years, weight at ages 20, 40 and 60 years, height and adiposity compared with peers at age 11  and waist circumference based on trouser size at age 20 and 5 years before interview (prediagnostic time‐points that we thought might be memorable for this more‐difficult to remember variable).We analysed the data by standard methods for case‐control studies,z scores to compare the strengths of these trends for exposures that were measured on different scales, and case‐case analyses to test for interaction. All analyses were conducted with STATA 16.0.We analysed linear trends in relative risks across exposure levels as a continuous variableThe study interviewers recorded at the end of the interview, their opinion of the reliability of the participant's responses. We then conducted sensitivity analyses in which we excluded subjects whose quality of responses were rated ‘not well’ or ‘very poorly’. We also conducted sensitivity analyses excluding from analysis men with known Klinefelter syndrome; excluding unmarried men rather than adjusting for marital status ; and adjusting for factors that are known or possible risk factors for breast cancer in men, although not clear confounders: chest radiotherapy, family history of breast cancer, testicular conditions and use of exogenous oestrogens or androgens.3In total, 3187 men diagnosed with breast cancer at ages under 80 years in England and Wales during the study period were reported to us by the cancer registries or consultants across the country. Four hundred and twenty seven (13.4%) of these had died before we could contact them, for 21 (0.7%) the consultant did not participate or we could not ascertain the consultant, 28 (0.9%) were deemed by their consultant as unsuitable to approach, 6 (0.2%) had emigrated and 707 (22.2%) declined or did not reply to the invitation to take part. This left 1998 (62.7%) who were interviewed and were the study cases. Most (71%) were aged 60 or older, 92% of tumours were invasive and the great majority (99% of known) were oestrogen receptor (ER) positive  participated in the study and of 1109 men approached to be Generations Study controls, 984 (89%) participated.P < .001) and from age 40 to 60  , with a stronger effect (z = 4.91) than for any of the above BMI changes . Adjustment for initial waist/height ratio increased this association , and BMI  remained independently significant . The participants gave individual written consent at recruitment.Table S1 Risk of breast cancer in men in relation to change in relative fatness/body mass index from ages 11 to 20, 20 to 40 and 40 to 60 years, adjusted for baseline fatness/BMITable S2 Risk of breast cancer in men in relation to change in waist/height ratio from age 20 to 5 years before interview, adjusted for waist/height ratio at age 20Table S3 Risk of invasive, and of in situ, breast cancer in men in relation to change in relative fatness/body mass index from ages 11 to 20, 20 to 40 and 40 to 60 yearsTable S4 Risk of invasive, and of in situ, breast cancer in men in relation to change in waist/height ratio from age 20 to 5 years before interviewTable S5 Risk of breast cancer in men in relation to change in relative fatness/body mass index from ages 11 to 20, 20 to 40 and 40 to 60 years, by HER‐2 expression statusTable S6 Risk of breast cancer in men in relation to change in waist/height ratio from age 20 to 5 years before interview, by HER‐2 expression statusClick here for additional data file.