Adverse health effects after breast cancer up to 14 years after diagnosis

BACKGROUND: The number of breast cancer survivors increases, but information about long-term adverse health effects in breast cancer survivors is sparse. We aimed to get an overview of the health effects for which survivors visit their general practitioner up to 14 years after diagnosis. METHODS: We retrieved data on 11,671 women diagnosed with breast cancer in 2000–2016 and 23,242 age and sex matched controls from the PSCCR-Breast Cancer, a database containing data about cancer diagnosis, treatment and primary healthcare. We built Cox regression models for 685 health effects, with time until the health effect as the outcome and survivor/control and cancer treatment as predictors. Models were built separately for four age groups (aged 18/44, 45/59, 60/74 and 75/89) and two follow-up periods (1/4 and 5/14 years after diagnosis). RESULTS: 229 health effects occurred statistically significantly more often in survivors than in controls (p < 0.05). Health effects varied by age, time since diagnosis and treatment, but coughing, respiratory and urinary infections, fatigue, sleep problems, osteoporosis and lymphedema were statistically significantly increased in breast cancer survivors. Osteoporosis and chest symptoms were associated with hormone therapy; respiratory and skin infections with chemotherapy and lymphedema and skin infections with axillary dissection. CONCLUSIONS: Breast cancer survivors may experience numerous adverse health effects up to 14 years after diagnosis. Insight in individual risks may assist healthcare professionals in managing patient expectations and improve monitoring, detection and treatment of adverse health effects

Development of machine learning models to predict cancer-related fatigue in Dutch breast cancer survivors up to 15 years after diagnosis

Purpose: To prevent (chronic) cancer-related fatigue (CRF) after breast cancer, it is important to identify survivors at risk on time. In literature, factors related to CRF are identified, but not often linked to individual risks. Therefore, our aim was to predict individual risks for developing CRF.Methods: Two pre-existing datasets were used. The Nivel-Primary Care Database and the Netherlands Cancer Registry (NCR) formed the Primary Secondary Cancer Care Registry (PSCCR). NCR data with Patient Reported Outcomes Following Initial treatment and Long-term Evaluation of Survivorship (PROFILES) data resulted in the PSCCR-PROFILES dataset. Predictors were patient, tumor and treatment characteristics, and pre-diagnosis health. Fatigue was GP-reported (PSCCR) or patient-reported (PSCCR-PROFILES). Machine learning models were developed, and performances compared using the C-statistic.Results: In PSCCR, 2224/12813 (17%) experienced fatigue up to 7.6 ± 4.4 years after diagnosis. In PSCCR-PROFILES, 254 (65%) of 390 patients reported fatigue 3.4 ± 1.4 years after diagnosis. For both, models predicted fatigue poorly with best C-statistics of 0.561 ± 0.006 (PSCCR) and 0.669 ± 0.040 (PSCCR-PROFILES).Conclusion: Fatigue (GP-reported or patient-reported) could not be predicted accurately using available data of the PSCCR and PSCCR-PROFILES datasets.Implications for Cancer Survivors: CRF is a common but underreported problem after breast cancer. We aimed to develop a model that could identify individuals with a high risk of developing CRF, ideally to help them prevent (chronic) CRF. As our models had poor predictive abilities, they cannot be used for this purpose yet. Adding patient-reported data as predictor could lead to improved results. Until then, awareness for CRF stays crucial

Results from a Large, Multinational Sample Using the Childhood Trauma Questionnaire

Childhood maltreatment has diverse, lifelong impact on morbidity and mortality. The Childhood Trauma Questionnaire (CTQ) is one of the most commonly used scales to assess and quantify these experiences and their impact. Curiously, despite very widespread use of the CTQ, scores on its Minimization-Denial (MD) subscale—originally designed to assess a positive response bias—are rarely reported. Hence, little is known about this measure. If response biases are either common or consequential, current practices of ignoring the MD scale deserve revision. Therewith, we designed a study to investigate 3 aspects of minimization, as defined by the CTQ’s MD scale: 1) its prevalence; 2) its latent structure; and finally 3) whether minimization moderates the CTQ’s discriminative validity in terms of distinguishing between psychiatric patients and community volunteers. Archival, item-level CTQ data from 24 multinational samples were combined for a total of 19,652 participants. Analyses indicated: 1) minimization is common; 2) minimization functions as a continuous construct; and 3) high MD scores attenuate the ability of the CTQ to distinguish between psychiatric patients and community volunteers. Overall, results suggest that a minimizing response bias—as detected by the MD subscale—has a small but significant moderating effect on the CTQ’s discriminative validity. Results also may suggest that some prior analyses of maltreatment rates or the effects of early maltreatment that have used the CTQ may have underestimated its incidence and impact. We caution researchers and clinicians about the widespread practice of using the CTQ without the MD or collecting MD data but failing to assess and control for its effects on outcomes or dependent variables

New actors and contested architectures in global migration governance : continuity and change

This article introduces the volume on New Actors and Contested Architectures in Global Migration Governance. It presents the aims and scope of the volume, followed by an overview of international cooperation in global migration governance, migration management and advocacy for migrants. We then discuss ‘new’ actors and how they maintain, contest or even alter established architectures and assemblages, followed by a presentation of the articles included in the volume. In conclusion, we reflect on the ways in which the COVID-19 pandemic may affect these dynamics.Research funder: Centre for Global Cooperation Research, University of Duisburg-Essen</p

Healthy elderly and influenza vaccination

In many countries, those at risk for complications due to influenza are invited for influenza vaccination, to prevent serious consequences for themselves and those around them. However, vaccination rates are decreasing. The first invitation for vaccination may provide an opportunity to convey ample information about the (dis)advantages of vaccination. We aimed to identify subgroups less likely to be vaccinated after their first invitation. Using data from 87 general practices participating in NIVEL Primary Care Database, we selected persons invited for vaccination for the first time because of their 60th birthday. Of 3.238 included persons, 78% were not vaccinated after their first invitation and in the vast majority (84%) this decision remained consistent over the next years. Men and those with fewer GP contacts were less likely to be vaccinated. This latter group is not easily reached by the GP, so maybe other ways should be considered to convey information about influenza vaccination

Identifying multimorbid patients with high care needs - A study based on electronic medical record data

Background: Patients with multimorbidity who frequently contact the general practice, use emergency care or have unplanned hospitalisations, may benefit from a proactive integrated care intervention. General practitioners are not always aware of who these ‘high need’ patients are. Electronic medical records are a potential source to identify them. Objectives: To find predictors of high care needs in general practice electronic medical records of patients with multimorbidity and assess their predictive value. Methods: General practice electronic medical records of 245,065 patients with ≥2 chronic diseases were linked to hospital claims data. Probit regression analysis was conducted to predict i) having at least 12 general practice contacts per year, ii) emergency department visit(s), and iii) unplanned hospitalisation(s). Predictors were patients’ age, sex, morbidity, health services and medication use in the previous year. Results: 11% of multimorbid patients had ≥12 general practice contacts, which could be reliably predicted by the number of contacts in the previous year (PPV 42%). The model containing all predictors had only slightly better predictive value (PPV 44%). Emergency department visits and unplanned hospitalisations (12% and 7% of multimorbid patients, respectively) could be predicted less accurately (PPV 27% and 20%). Those with frequent contact with the general practice hardly overlapped with ED visitors (29%) or persons with unplanned hospitalisations (17%). Conclusion: Among multimorbid populations various ‘high need’ groups exist. Patients with high needs for general practice care can be identified by their previous use of general practice care. To identify frequent ED visitors and persons with unplanned hospitalisations, additional information is needed

Erratum: Corrigendum to “Health care utilization of patients with multiple chronic diseases in The Netherlands: Differences and underlying factors” (European Journal of Internal Medicine (2015) 26(3) (190–196) (S0953620515000412) (10.1016/j.ejim.2015.02.006))

The authors regret that the printed version of the above article contained a number of errors. A revised, correct and final version has been published online under the same title, with the following authors: Petra Hopman, Marianne J. Heins, Joke Korevaar, Mieke Rijken and François G. Schellevis. The printed version follows. The authors would like to apologise for any inconvenience caused