Biologics registers in RA: methodological aspects, current role and future applications

The beginning of the 21st century saw a biopharmaceutical revolution in the treatment of inflammatory rheumatic diseases, particularly rheumatoid arthritis. The fast-evolving use of biologic therapies highlighted the need to develop registers at national and international levels with the aim of collecting long-term data on patient outcomes. Over the past 15 years, many biologics registers have contributed a wealth of data and provided robust and reliable evidence on the use, effectiveness and safety of these therapies. The unavoidable challenges posed by the continuous introduction of new therapies, particularly with regard to understanding their long-term safety, highlights the importance of learning from experience with established biologic therapies. In this Perspectives article, the role of biologics registers in bridging the evidence gap between efficacy in clinical trials and real-world effectiveness is discussed, with a focus on methodological aspects of registers, their unique features and challenges and their role going forward

Age-period-cohort modelling of type 1 diabetes incidence rates among children included in the EURODIAB 25-year follow-up study

AIMS Specific patterns in incidence may reveal environmental explanations for type 1 diabetes incidence. We aimed to study type 1 diabetes incidence in European childhood populations to assess whether an increase could be attributed to either period or cohort effects. METHODS Nineteen EURODIAB centres provided single year incidence data for ages 0-14 in the 25-year period 1989-2013. Case counts and person years were classified by age, period and cohort (APC) in 1-year classes. APC Poisson regression models of rates were fitted using restricted cubic splines for age, period and cohort per centre and sex. Joint models were fitted for all centres and sexes, to find a parsimonious model. RESULTS A total of 57,487 cases were included. In ten and seven of the 19 centres the APC models showed evidence of nonlinear cohort effects or period effects, respectively, in one or both sexes and indications of sex-specific age effects. Models showed a positive linear increase ranging from approximately 0.6 to 6.6%/year. Centres with low incidence rates showed the highest overall increase. A final joint model showed incidence peak at age 11.6 and 12.6 for girls and boys, respectively, and the rate-ratio was according to sex below 1 in ages 5-12. CONCLUSION There was reasonable evidence for similar age-specific type 1 diabetes incidence rates across the EURODIAB population and peaks at a younger age for girls than boys. Cohort effects showed nonlinearity but varied between centres and the model did not contribute convincingly to identification of environmental causes of the increase

Socioeconomic position and survival after cervical cancer:influence of cancer stage, comorbidity and smoking among Danish women diagnosed between 2005 and 2010

BACKGROUND: In an attempt to decrease social disparities in cancer survival, it is important to consider the mechanisms by which socioeconomic position influences cancer prognosis. We aimed to investigate whether any associations between socioeconomic factors and survival after cervical cancer could be explained by socioeconomic differences in cancer stage, comorbidity, lifestyle factors or treatment. METHODS: We identified 1961 cases of cervical cancer diagnosed between 2005 and 2010 in the Danish Gynaecological Cancer database, with information on prognostic factors, treatment and lifestyle. Age, vital status, comorbidity and socioeconomic data were obtained from nationwide administrative registers. Associations between socioeconomic indicators (education, income and cohabitation status) and mortality by all causes were analysed in Cox regression models with inclusion of possible mediators. Median follow-up time was 3.0 years (0.01–7.0). RESULTS: All cause mortality was higher in women with shorter rather than longer education (hazard ratio (HR), 1.46; 1.20–1.77), among those with lower rather than higher income (HR, 1.32; 1.07–1.63) and among women aged<60 years without a partner rather than those who cohabited (HR, 1.60; 1.29–1.98). Socioeconomic differences in survival were partly explained by cancer stage and less by comorbidity or smoking (stage- and comorbidty- adjusted HRs being 1.07; 0.96–1.19 for education and 1.15; 0.86–1.52 for income). CONCLUSION: Socioeconomic disparities in survival after cervical cancer were partly explained by socioeconomic differences in cancer stage. The results point to the importance of further investigations into reducing diagnosis delay among disadvantaged groups