Previous studies have shown that reproductive factors are differentially associated with breast cancer (BC) risk by subtypes. The aim of this study was to investigate associations between reproductive factors and BC subtypes, and whether these vary by age at diagnosis. We used pooled data on tumor markers (estrogen and progesterone receptor, human epidermal growth factor receptor-2 (HER2)) and reproductive risk factors (parity, age at first full-time pregnancy (FFTP) and age at menarche) from 28,095 patients with invasive BC from 34 studies participating in the Breast Cancer Association Consortium (BCAC). In a case-only analysis, we used logistic regression to assess associations between reproductive factors and BC subtype compared to luminal A tumors as a reference. The interaction between age and parity in BC subtype risk was also tested, across all ages and, because age was modeled non-linearly, specifically at ages 35, 55 and 75 years. Parous women were more likely to be diagnosed with triple negative BC (TNBC) than with luminal A BC, irrespective of age (OR for parity = 1.38, 95% CI 1.16–1.65, p = 0.0004; p for interaction with age = 0.076). Parous women were also more likely to be diagnosed with luminal and non-luminal HER2-like BCs and this effect was slightly more pronounced at an early age (p for interaction with age = 0.037 and 0.030, respectively). For instance, women diagnosed at age 35 were 1.48 (CI 1.01–2.16) more likely to have luminal HER2-like BC than luminal A BC, while this association was not significant at age 75 (OR = 0.72, CI 0.45–1.14). While age at menarche was not significantly associated with BC subtype, increasing age at FFTP was non-linearly associated with TNBC relative to luminal A BC. An age at FFTP of 25 versus 20 years lowered the risk for TNBC (OR = 0.78, CI 0.70–0.88, p < 0.0001), but this effect was not apparent at a later FFTP. Our main findings suggest that parity is associated with TNBC across all ages at BC diagnosis, whereas the association with luminal HER2-like BC was present only for early onset BC.

Background: The EBMeDS system is the computerized clinical decision support (CCDS) system of EBPNet, a national computerized point-of-care information service in Belgium. There is no clear evidence of more complex CCDS systems to manage chronic diseases in primary care practices (PCPs). The objective of this study was to assess the effectiveness of EBMeDS use in improving diabetes care. Methods: A cluster-randomized trial with before-and-after measurements was performed in Belgian PCPs over 1 year, from May 2017 to May 2018. We randomly assigned 51 practices to either the intervention group (IG), to receive the EBMeDS system, or to the control group (CG), to receive usual care. Primary and secondary outcomes were the 1-year pre- to post-implementation change in HbA1c, LDL cholesterol, and systolic and diastolic blood pressure. Composite patient and process scores were calculated. A process evaluation was added to the analysis. Results were analyzed at 6 and 12 months. Linear mixed models and logistic regression models based on generalized estimating equations were used where appropriate. Results: Of the 51 PCPs that were enrolled and randomly assigned (26 PCPs in the CG and 25 in the IG), 29 practices (3815 patients) were analyzed in the study: 2464 patients in the CG and 1351 patients in the IG. No change differences existed between groups in primary or secondary outcomes. Change difference between CG and IG after 1-year follow-up was − 0.09 (95% CI − 0.18; 0.01, p-value = 0.06) for HbA1c; 1.76 (95% CI − 0.46; 3.98, p-value = 0.12) for LDL cholesterol; and 0.13 (95% CI − 0.91; 1.16, p-value = 0.81) and 0.12 (95% CI − 1.25;1.49, p-value = 0.86) for systolic and diastolic blood pressure respectively. The odds ratio of the IG versus the CG for the probability of no worsening and improvement was 1.09 (95% CI 0.73; 1.63, p-value = 0.67) for the process composite score and 0.74 (95% CI 0.49; 1.12, p-value = 0.16) for the composite patient score. All but one physician was satisfied with the EBMeDS system. Conclusions: The CCDS system EBMeDS did not improve diabetes care in Belgian primary care. The lack of improvement was mainly caused by imperfections in the organizational context of Belgian primary care for chronic disease management and shortcomings in the system requirements for the correct use of the EBMeDS system (e.g., complete structured records). These shortcomings probably caused low-use rates of the system. Trial registration: ClinicalTrials.gov, NCT01830569, Registered 12 April 2013.

Aims To determine the impact of depressive symptoms on pregnancy outcomes and postpartum quality of life in women with gestational diabetes mellitus (GDM) and normal glucose tolerance (NGT). Methods 1843 women from a prospective cohort study received universal GDM screening with an oral glucose tolerance test (OGTT). The Center for Epidemiologic Studies–Depression questionnaire was completed before GDM diagnosis was communicated and in GDM women in early postpartum. All participants completed the 36-Item Short Form Health Survey (SF-36) health survey postpartum. Results Women who developed GDM (231; 12.5%) had significantly more often depressive symptoms than NGT (1612; 87.5%) women [21.3% (48) vs 15.1% (239), odds ratio (OR) 1.52, 95% confidence interval (CI) (1.08-2.16), P = 0.017]. Compared to GDM women without depressive symptoms, depressed GDM women attended less often the postpartum OGTT [68.7% (33) vs 87.6% (155), P = 0.002], remained more often depressed [37.1% (13) vs 12.4% (19), P < 0.001], and had lower SF-36 scores postpartum. There were no significant differences in pregnancy outcomes between both groups. Rates of labor inductions were significantly higher in the NGT group with depressive symptoms compared to the nondepressed NGT group [31.7% (75) vs 24.7% (330), adjusted OR (aOR) 1.40, 95% CI (1.01-1.93), P = 0.041]. NGT women with depressive symptoms had lower SF-36 scores (P < 0.001) postpartum compared to nondepressed NGT women. Conclusions Women with antenatal symptoms of depression develop more often GDM. GDM women with depressive symptoms remain more often depressed postpartum with lower quality of life. NGT women with depressive symptoms have higher rates of labor inductions and lower quality of life postpartum compared to nondepressed NGT women.

Background: Exact numbers of breast cancer recurrences are currently unknown at the population level, because they are challenging to actively collect. Previously, real-world data such as administrative claims have been used within expert- or data-driven (machine learning) algorithms for estimating cancer recurrence. We present the first systematic review and meta-analysis, to our knowledge, of publications estimating breast cancer recurrence at the population level using algorithms based on administrative data.Methods: The systematic literature search followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We evaluated and compared sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of algorithms. A random-effects meta-analysis was performed using a generalized linear mixed model to obtain a pooled estimate of accuracy.Results: Seventeen articles met the inclusion criteria. Most articles used information from medical files as the gold standard, defined as any recurrence. Two studies included bone metastases only in the definition of recurrence. Fewer studies used a model-based approach (decision trees or logistic regression) (41.2%) compared with studies using detection rules without specified model (58.8%). The generalized linear mixed model for all recurrence types reported an accuracy of 92.2% (95% confidence interval = 88.4% to 94.8%).Conclusions: Publications reporting algorithms for detecting breast cancer recurrence are limited in number and heterogeneous. A thorough analysis of the existing algorithms demonstrated the need for more standardization and validation. The meta-analysis reported a high accuracy overall, which indicates algorithms as promising tools to identify breast cancer recurrence at the population level. The rule-based approach combined with emerging machine learning algorithms could be interesting to explore in the future.