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In 2019, the Innovative Medicines Initiative (IMI) funded the ConcePTION project-Building an ecosystem for better monitoring and communicating safety of medicines use in pregnancy and breastfeeding: validated and regulatory endorsed workflows for fast, optimised evidence generation-with the vision that there is a societal obligation to rapidly reduce uncertainty about the safety of medication use in pregnancy and breastfeeding. The present paper introduces the set of concepts used to describe the European data sources involved in the ConcePTION project and illustrates the ConcePTION Common Data Model (CDM), which serves as the keystone of the federated ConcePTION network. Based on data availability and content analysis of 21 European data sources, the ConcePTION CDM has been structured with six tables designed to capture data from routine healthcare, three tables for data from public health surveillance activities, three curated tables for derived data on population (e.g., observation time and mother-child linkage), plus four metadata tables. By its first anniversary, the ConcePTION CDM has enabled 13 data sources to run common scripts to contribute to major European projects, demonstrating its capacity to facilitate effective and transparent deployment of distributed analytics, and its potential to address questions about utilization, effectiveness, and safety of medicines in special populations, including during pregnancy and breastfeeding, and, more broadly, in the general population.

Overdiagnosis is a harmful consequence of screening which is particularly challenging to estimate. An unbiased setting to measure overdiagnosis in breast cancer screening requires comparative data from a screened and an unscreened cohort for at least 30 years. Such randomised data will not become available, leaving us with observational data over shorter time periods and outcomes of modelling. This collaborative effort of the International Cancer Screening Network quantified the variation in estimated breast cancer overdiagnosis in organised programmes with evaluation of both observed and simulated data, and presented examples of how modelling can provide additional insights. Reliable observational data, analysed with study design accounting for methodological pitfalls, and modelling studies with different approaches, indicate that overdiagnosis accounts for less than 10% of invasive breast cancer cases in a screening target population of women aged 50 to 69. Estimates above this level are likely to derive from inaccuracies in study design. The widely discrepant estimates of overdiagnosis reported from observational data could substantially be reduced by use of a cohort study design with at least 10 years of follow-up after screening stops. In contexts where concomitant opportunistic screening or gradual implementation of screening occurs, and data on valid comparison groups are not readily available, modelling of screening intervention becomes an advantageous option to obtain reliable estimates of breast cancer overdiagnosis.

Objective: To evaluate the impact of a cervical cancer (CC) diagnosis on use of health care and prescription drugs. Methods: This population-based register-study included Danish women aged 23–59 years and diagnosed with CC in 2001–2005. Women with a cervical screening outcome were used as comparison group. We obtained number of contacts to general practitioners (GPs), hospitals, psychologists/psychiatrists and defined daily doses (DDD) of analgesic/psychotropic prescription drugs. A difference-in-differences-design was used to estimate effect of a CC diagnosis on health-care use from five-year periods before and after the diagnosis/screening outcome. Results: In total, 926 women with CC and 1,004,759 women without cancer were included. In five years following the date of CC diagnosis, CC patients had increased their use of GPs with 8.6 (95% CI 4.8–12.4) contacts more than women in the comparison group, and with 4.12 (95% CI 3.99–4.25) more hospital contacts. In contrast, use of psychologists/psychiatrists was low and largely unaffected by the CC diagnosis. For use of prescription drugs, analgesics increased with 80 (95% CI 60–100) DDD more in CC patients than in comparison women, and for psychotropics with 304 (95% CI 261–347) DDD more. Conclusions: A CC diagnosis was followed by an increase in use of GPs, hospitals, and analgesic/psychotropic prescription drugs, while use of psychologist/psychiatrist was largely unaffected. This pattern may indicate that pain/mental health concerns after CC either persisted or were alleviated by other means only.

Background: Although breast cancer screening reduces breast cancer mortality at the population level, subgroups of women may benefit differently. We investigated the impact of health status on the effect of breast cancer screening. Methods: The study included 181 299 women invited in two population-based screening programs in Denmark and 1 526 446 control subjects, followed from April 1981 to December 2014. Poisson regressions were used to compare the observed breast cancer mortality rate in women invited to screening with the expected rate in the absence of screening among women with and without chronic diseases. Chronic diseases were defined as any diagnosis in the Charlson Comorbidity Index during 4 years before the first invitation to screening. Results: Almost 10% of women had chronic diseases before first invitation to screening. Whereas we observed a reduction in breast cancer mortality following invitation to screening of 28% (95% CI, 20% to 35%) among women without chronic diseases, only a 7% (95% CI, −39% to 37%) reduction was seen for women with chronic diseases (P-value for interaction =.22). For participants, the reduction, corrected for selection bias, was 35% (95% CI 16% to 49%) for women without, and 4% (95% CI −146% to 62%) for women with chronic diseases (P-value for interaction =.43). Conclusion: Our data indicate a marginal effect of mammography screening on breast cancer mortality in women with chronic diseases. If our results are confirmed in other populations, the presence of chronic diseases will be an important factor to take into consideration in personalized screening.

Purpose Randomized, controlled trials showed that screening reduces breast cancer mortality rates, but some recent observational studies have concluded that programmatic screening has had minor effect on breast cancer mortality rates. This apparent contradiction might be explained by the use of aggregated data in observational studies. We assessed the long-term effect of screening using individual-level data. Materials and Methods Using data from mammography screening in the Copenhagen and Danish national registers, we compared the observed breast cancer mortality rate in women invited to screening with the expected rate in absence of screening. The effect was examined using the "naïve model," which included all breast cancer deaths; the "follow-up model," which counted only breast cancer deaths in women diagnosed after their first invitation to screening; and the "evaluation model," which is similar to the follow-up model during screening age, but after screening age, which counted only breast cancer deaths and person-years in women diagnosed during screening age. Results We included 18,781,292 person-years, 976,743 of which were from women invited to screening. The naïve and follow-up models showed, respectively, 10% and 11% reduction in breast cancer mortality after invitation to screening. However, many breast cancer deaths occurred in women whose cancer was diagnosed when they were no longer eligible for screening. Accounting for this dilution, the evaluation model showed a 20% (95% CI, 10% to 29%) reduction in breast cancer mortality after invitation to screening. Conclusion Screening had a clear long-term beneficial effect with a 20% reduction in breast cancer-associated mortality in the invited population. However, this effect was, by nature, restricted to breast cancer deaths in women who could potentially benefit from screening. Our study highlights the complexity in evaluating the long-term effect of breast cancer screening from observational data.

Overdiagnosis in breast cancer screening is an important issue. A recent study from Denmark concluded that one in three breast cancers diagnosed in screening areas in women aged 50-69 years were overdiagnosed. The purpose of this short communication was to disentangle the study's methodology in order to evaluate the soundness of this conclusion. We found that both the use of absolute differences as opposed to ratios; the sole focus on non-advanced tumours and the crude allocation of tumours and person-years by screening history for women aged 70-84 years, all contributed to the very high estimate of overdiagnosis. Screening affects cohorts of screened women. Danish registers allow very accurate mapping of the fate of every woman. We should be past the phase where studies of overdiagnosis are based on the fixed age groups from routine statistics.