Danish registers contain information on many important health and social issues. Because all Danish citizens have a unique personal identification number, linkage at the individual level between these nationwide registers and other data sources is possible and feasible. In this paper we briefly introduce selected Danish registers and the data structure and requirements for getting access to data at Statistics Denmark, which is the main provider of register data. We introduce the Danish Data Archive and briefly present the Act on Processing of Personal Data, which is the legal foundation for analyses of register-based data in Denmark.

Introduction: Statistics Denmark has a number of registers about labour market affiliation, which includes information on employees, establishments and the relation between employees and establishments. Content: The registers describe a person’s attachment to the labour market, information on the establishments and information on the relations between persons and establishments. The registers presented either include information at one specific point in time each year (end of November) or during the year. Validity and coverage: Both the validity and coverage of the registers are considered to be high. Conclusion: These registers provide valuable information on personal labour market affiliation and can be used to study determinants and consequences of labour market affiliation.

Objective To describe the pattern of development of incidence of multiple sclerosis (MS) over 60 years in Denmark with age-period-cohort analyses and seasonality of birth. Methods Data on virtually all patients with onset of MS have since 1950 been prospectively recorded and kept in the Danish Multiple Sclerosis Registry with multiple sources of notification. Annualized incidence rates per 100,000 were directly standardized to the European Standard Population. Results We have registered 19,536 cases with clinical onset of confirmed MS in Denmark from 1950 to 2009. From the 1950–1959 to the 2000–2009 onset period, incidence more than doubled in women, with an increase from 5.91 (95% confidence interval [CI]: 5.60–6.24) to 12.33 (95% CI: 11.91–12.75) per 100,000 per year compared with a modest 24% increase in men from 4.52 (95% CI: 4.24–4.81) to 6.08 (95% CI: 5.79–6.38). With age at onset of 50 years and older, incidence increased with a factor 4.30 in women and 2.72 in men. The female/male sex ratio increased over time and with year of birth. Age-period-cohort analysis revealed a significant birth cohort effect in addition to the age and period effect. We found no statistically significant seasonality of births. Conclusion The incidence of MS has doubled in women, most pronounced with late onset, and has only modestly increased in men. Lifestyle changes in the female population that could include fewer childbirths, increased occurrence of obesity, and increased cigarette consumption may have a role.

Within the fields of epidemiology, interventions research and social sciences researchers are often faced with the challenge of decomposing the effect of an exposure into different causal pathways working through defined mediator variables. The goal of such analyses is often to understand the mechanisms of the system or to suggest possible interventions. The case of a single mediator, thus implying only 2 causal pathways (direct and indirect) from exposure to outcome, has been extensively studied. By using the framework of counterfactual variables, researchers have established theoretical properties and developed powerful tools. However, in practical problems, it is not uncommon to have several distinct causal pathways from exposure to outcome operating through different mediators. In this article, we suggest a widely applicable approach to quantifying and ranking different causal pathways. The approach is an extension of the natural effect models proposed by Lange et al. (Am J Epidemiol. 2012;176(3):190–195). By allowing the analysis of distinct multiple pathways, the suggested approach adds to the capabilities of modern mediation techniques. Furthermore, the approach can be implemented using standard software, and we have included with this article implementation examples using R (R Foundation for Statistical Computing, Vienna, Austria) and Stata software (StataCorp LP, College Station, Texas).

Background: Digital health interventions for managing chronic conditions have great potential. However, the benefits and harms are still unclear. Objective: This systematic review and meta-analysis aimed to investigate the benefits and harms of digital health interventions in promoting physical activity in people with chronic conditions. Methods: We searched the MEDLINE, Embase, CINAHL, and Cochrane Central Register of Controlled Trials databases from inception to October 2022. Eligible randomized controlled trials were included if they used a digital component in physical activity promotion in adults with ≥1 of the following conditions: depression or anxiety, ischemic heart disease or heart failure, chronic obstructive pulmonary disease, knee or hip osteoarthritis, hypertension, or type 2 diabetes. The primary outcomes were objectively measured physical activity and physical function (eg, walk or step tests). We used a random effects model (restricted maximum likelihood) for meta-analyses and meta-regression analyses to assess the impact of study-level covariates. The risk of bias was assessed using the Cochrane Risk of Bias 2 tool, and the certainty of the evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation. Results: Of 14,078 hits, 130 randomized controlled trials were included. Compared with usual care or minimal intervention, digital health interventions increased objectively measured physical activity (end of intervention: standardized mean difference [SMD] 0.29, 95% CI 0.21-0.37; follow-up: SMD 0.17, 95% CI 0.04-0.31) and physical function (end of intervention: SMD 0.36, 95% CI 0.12-0.59; follow-up: SMD 0.29, 95% CI 0.01-0.57). The secondary outcomes also favored the digital health interventions for subjectively measured physical activity and physical function, depression, anxiety, and health-related quality of life at the end of the intervention but only subjectively measured physical activity at follow-up. The risk of nonserious adverse events, but not serious adverse events, was higher in the digital health interventions at the end of the intervention, but no difference was seen at follow-up. Conclusions: Digital health interventions improved physical activity and physical function across various chronic conditions. Effects on depression, anxiety, and health-related quality of life were only observed at the end of the intervention. The risk of nonserious adverse events is present during the intervention, which should be addressed. Future studies should focus on better reporting, comparing the effects of different digital health solutions, and investigating how intervention effects are sustained beyond the end of the intervention. Trial Registration: PROSPERO CRD42020189028; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=189028