To evaluate how recurrent COVID-19 infections influence the clinical course of patients with chronic obstructive pulmonary disease (COPD), focusing on moderate-to-severe symptom flare-ups, all-cause mortality and long covid.

Nationwide retrospective cohort study.

Korean Health Insurance Review and Assessment database covering the entire Korean population between January 2020 and December 2023.

A total of 313 760 patients aged ≥40 years who met an established operational definition of COPD based on diagnostic codes and inhaled therapy prescriptions. Patients were stratified by the number of COVID-19 events: none, one, two or three or more.

The primary outcomes were moderate-to-severe COPD exacerbations and all-cause mortality. The secondary outcome was long covid, defined by WHO criteria using International Classification of Diseases (ICD)-10 codes persisting ≥2 months within 3 months after infection.

Among 313 760 patients, 154 095 (49.1 %) experienced at least one COVID-19 event. COVID-19 infection was associated with increased risk of exacerbations (adjusted HR (aHR) 1.64, 95% CI 1.62 to 1.66) and mortality (aHR 2.25, 95 % CI 2.19 to 2.31). Risk rose progressively with repeated infections, reaching an aHR of 2.41 for exacerbations and 2.93 for mortality after three or more events. Long covid was more frequent in patients with multiple infections, but most cases occurred after the first event, with diminishing occurrence after subsequent infections.

Recurrent COVID-19 infections in patients with COPD were linked to progressively higher risk of exacerbations and mortality, whereas the burden of long covid was greatest after the first infection. Preventing the initial infection and reducing reinfection risk remain critical components of COPD care in the post-COVID-19 era.

Traditional epidemiological approaches usually assume a constant relationship between cumulative exposure and disease, which implies that exposure duration and intensity contribute equally to the studied outcome. But individuals with the same cumulative exposure but different temporal exposure patterns may show different risks. Trajectory classification is a good way to assess exposure–risk associations and leads to a better understanding of lifetime variability in exposure levels. Therefore, this study aimed to estimate lung cancer risk according to the exposure trajectory classes on welding fumes and cigarette smoking.

Two population-based German case–control studies.

3498 male lung cancer cases and 3539 male control subjects.

Separate latent class mixed models (LCMM) were determined to identify profiles of exposure trajectories of cigarette smoking and occupational exposure to welding fumes. To investigate the risk of lung cancer by class membership, ORs with 95% CI were estimated via multiple logistic regression analyses.

LCMM each identified four latent classes of smoking and welding-fume exposure. Classes of smokers showed much higher risk of lung cancer compared with never smokers or subjects exposed to welding fumes. Smokers in one class characterised with the highest exposure over the past 10 years had the highest adjusted lung cancer risk (OR=39; 95% CI 29 to 53). For welding, the highest lung cancer risks were found for the class in which exposure to welding fumes in the past 10 years prior to the diagnosis of lung cancer was highest and the duration of welding was also quite high (OR=1.71; 95% CI 0.92 to 3.15).

In summary, LCMM opens a new perspective on dose–effect relationships and could be employed to complement established epidemiological methods.

The necessity of enhancing resuscitation training has been encouraged by The International Liaison Committee on Resuscitation and the American Heart Association to reduce mortality, disability and healthcare costs. Resuscitation training is a complicated approach that encompasses various components and their mixture. It is essential to identify the most effective of these components and their combinations, to measure the corresponding effect size and to understand which participant groups may enjoy the greatest advantage.

We will systematically search 12 databases and two clinical trial registries for randomised controlled trials (RCTs) that examine different resuscitation training methods from inception to April 2025. The analysis will be carried out using the standard network meta-analysis and component network meta-analysis models. Resuscitation skills of staff will be the primary outcome of this analysis. Paired reviewers will independently screen and extract data. A consensus will be sought with the principal investigators to resolve any disagreements that cannot be achieved through regular meetings. Each intervention in each RCT will be decomposed according to its constituent components, such as delivery method, interactivity, teamwork, digitalisation and type of simulator. The analysis will be conducted using the frequentist and bayesian approach in the R environment. RoB V.2.0 and Confidence in Network Meta-Analysis will, respectively, be used to assess the risk of bias and the certainty of the evidence.

As we will use only aggregated secondary data without individual identities, ethical approval is not required. Results of this review will be shared through a peer-reviewed publication and presentation of papers at any relevant conferences.

CRD42024532878