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Mortality from cardiovascular disease (CVD), second tumours, and other causes is of clinical interest in the long-term follow-up of breast cancer (BC) patients. Using a cohort of BC patients (N = 6758) from the cancer registries of Girona and Tarragona (north-eastern Spain), we studied the 10-year probabilities of death due to BC, other cancers, and CVD according to stage at diagnosis and hormone receptor (HR) status. Among the non-BC causes of death (N = 720), CVD (N = 218) surpassed other cancers (N = 196). The BC cohort presented a significantly higher risk of death due to endometrial and ovarian cancers than the general population. In Stage I, HR- patients showed a 1.72-fold higher probability of all-cause death and a 6.11-fold higher probability of breast cancer death than HR+ patients. In Stages II-III, the probability of CVD death (range 3.11% to 3.86%) surpassed that of other cancers (range 0.54% to 3.11%). In Stage IV patients, the probability of death from any cancer drove the mortality risk. Promoting screening and preventive measures in BC patients are warranted, since long-term control should encompass early detection of second neoplasms, ruling out the possibility of late recurrence. In patients diagnosed in Stages II-III at an older age, surveillance for preventing late cardiotoxicity is crucial.

Breast cancer (BC) is globally the most frequent cancer in women. Adherence to endocrine therapy (ET) in hormone-receptor-positive BC patients is active and voluntary for the first five years after diagnosis. This study examines the impact of adherence to ET on 10-year excess mortality (EM) in patients diagnosed with Stages I to III BC (N = 2297). Since sample size is an issue for estimating age- and stage-specific survival indicators, we developed a method, ComSynSurData, for generating a large synthetic dataset (SynD) through probabilistic graphical modeling of the original cohort. We derived population-based survival indicators using a Bayesian relative survival model fitted to the SynD. Our modeling showed that hormone-receptor-positive BC patients diagnosed beyond 49 years of age at Stage I or beyond 59 years at Stage II do not have 10-year EM if they follow the prescribed ET regimen. This result calls for developing interventions to promote adherence to ET in patients with hormone receptor-positive BC and in turn improving cancer survival. The presented methodology here demonstrates the potential use of probabilistic graphical modeling for generating reliable synthetic datasets for validating population-based survival indicators when sample size is an issue.

Background: Short message service (SMS) based interventions are widely used in healthcare and have shown promising results to improve cancer screening programs. However, more research is still needed to implement SMS in the screening process. We present a study protocol to assess the impact on health and economics of three targeted SMS-based interventions in population-based cancer screening programs., Methods/design: The M-TICs study is a randomized controlled trial with a formal process evaluation. Participants aged 50-69 years identified as eligible from the colorectal cancer (CRC) and breast cancer (BC) screening program of the Catalan Institute of Oncology (Catalonia, Spain) will be randomly assigned to receive standard invitation procedure (control group) or SMS-based intervention to promote participation. Two interventions will be conducted in the CRC screening program: 1) Screening invitation reminder: Those who do not participate in the CRC screening within 6 weeks of invite will receive a reminder (SMS or letter); 2) Reminder to complete and return fecal immunochemical test (FIT) kit: SMS reminder versus no intervention to individuals who have picked up a FIT kit at the pharmacy and they have not returned it after 14 days. The third intervention will be performed in the BC screening program. Women who had been screened previously will receive an SMS invitation or a letter invitation to participate in the screening. As a primary objective we will assess the impact on participation for each intervention. The secondary objectives will be to analyze the cost-effectiveness of the interventions and to assess participants' perceptions., Expected Results: The results from this randomized controlled trial will provide important empirical evidence for the use of mobile phone technology as a tool for improving population-based cancer screening programs. These results may influence the cancer screening invitation procedure in future routine practice., Trial Registration: Registry: NCT04343950 (04/09/2020); clinicaltrials.gov., Competing Interests: Authors declare funding from ISCIII; no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work.

Background: Two common issues may arise in certain population-based breast cancer (BC) survival studies: I) missing values in a survivals' predictive variable, such as "Stage" at diagnosis, and II) small sample size due to "imbalance class problem" in certain subsets of patients, demanding data modeling/simulation methods., Methods: We present a procedure, ModGraProDep, based on graphical modeling (GM) of a dataset to overcome these two issues. The performance of the models derived from ModGraProDep is compared with a set of frequently used classification and machine learning algorithms (Missing Data Problem) and with oversampling algorithms (Synthetic Data Simulation). For the Missing Data Problem we assessed two scenarios: missing completely at random (MCAR) and missing not at random (MNAR). Two validated BC datasets provided by the cancer registries of Girona and Tarragona (northeastern Spain) were used., Results: In both MCAR and MNAR scenarios all models showed poorer prediction performance compared to three GM models: the saturated one (GM.SAT) and two with penalty factors on the partial likelihood (GM.K1 and GM.TEST). However, GM.SAT predictions could lead to non-reliable conclusions in BC survival analysis. Simulation of a "synthetic" dataset derived from GM.SAT could be the worst strategy, but the use of the remaining GMs models could be better than oversampling., Conclusion: Our results suggest the use of the GM-procedure presented for one-variable imputation/prediction of missing data and for simulating "synthetic" BC survival datasets. The "synthetic" datasets derived from GMs could be also used in clinical applications of cancer survival data such as predictive risk analysis., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Objective: To evaluate the differences between autochthonous and allochthonous women's participation in a breast cancer screening programme., Method: Retrospective study based on data from the Breast Cancer Screening Programme of the province of Tarragona (2008-2015). The sample is the target population of the programme with known country of origin., Results: Cohort of 40,824 women. Allochthonous women participate less than autochthonous women (41.8% vs. 72.3%) although they have a similar global detection rate to the latter but with differences according to the human development index of their country of origin. Both groups present similar tumour stages on detection (p=.59)., Conclusions: Strategies specifically aimed at the immigrant population are required to improve their participation in breast cancer screening., (Copyright © 2018 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.)

Benign breast disease increases the risk of breast cancer. This association has scarcely been evaluated in the context of breast cancer screening programs although it is a prevalent finding in mammography screening. We assessed the association of distinct categories of benign breast disease and subsequent risk of breast cancer, as well as the influence of a family history of breast cancer. A retrospective cohort study was conducted in 545,171 women aged 50-69 years biennially screened for breast cancer in Spain. The median of follow-up was 6.1 years. The age-adjusted rate ratio (RR) of breast cancer for women with benign breast disease, histologically classified into nonproliferative and proliferative disease with and without atypia, compared with women without benign breast disease was estimated by Poisson regression analysis. A stratified analysis by family history of breast cancer was performed in a subsample. All tests were two-sided. The age-adjusted RR of breast cancer after diagnosis of benign breast disease was 2.51 (95 % CI: 2.14-2.93) compared with women without benign breast disease. The risk was higher in women with proliferative disease with atypia (RR = 4.56, 95 % CI: 2.06-10.07) followed by those with proliferative disease without atypia (RR = 3.58; 95 % CI = 2.61-4.91). Women with nonproliferative disease and without a family history of breast cancer remained also at increased risk of cancer (OR = 2.23, 95 % CI: 1.86-2.68). An increased risk of breast cancer was observed among screening participants with proliferative or nonproliferative benign breast disease, regardless of a family history of breast cancer. This information may be useful to explore risk-based screening strategies.

Background: Breast cancer mortality has experienced important changes over the last century. Breast cancer occurs in the presence of other competing risks which can influence breast cancer incidence and mortality trends. The aim of the present work is: 1) to assess the impact of breast cancer deaths among mortality from all causes in Catalonia (Spain), by age and birth cohort and 2) to estimate the risk of death from other causes than breast cancer, one of the inputs needed to model breast cancer mortality reduction due to screening or therapeutic interventions., Methods: The multi-decrement life table methodology was used. First, all-cause mortality probabilities were obtained by age and cohort. Then mortality probability for breast cancer was subtracted from the all-cause mortality probabilities to obtain cohort life tables for causes other than breast cancer. These life tables, on one hand, provide an estimate of the risk of dying from competing risks, and on the other hand, permit to assess the impact of breast cancer deaths on all-cause mortality using the ratio of the probability of death for causes other than breast cancer by the all-cause probability of death., Results: There was an increasing impact of breast cancer on mortality in the first part of the 20th century, with a peak for cohorts born in 1945-54 in the 40-49 age groups (for which approximately 24% of mortality was due to breast cancer). Even though for cohorts born after 1955 there was only information for women under 50, it is also important to note that the impact of breast cancer on all-cause mortality decreased for those cohorts., Conclusion: We have quantified the effect of removing breast cancer mortality in different age groups and birth cohorts. Our results are consistent with US findings. We also have obtained an estimate of the risk of dying from competing-causes mortality, which will be used in the assessment of the effect of mammography screening on breast cancer mortality in Catalonia.