The association between pulmonary sequelae and markers of disease severity, as well as pro-fibrotic mediators, were studied in 108 patients 3 months after hospital admission for COVID-19. The COPD assessment test (CAT-score), spirometry, diffusion capacity of the lungs (DLCO), and chest-CT were performed at 23 Norwegian hospitals included in the NOR-SOLIDARITY trial, an open-labelled, randomised clinical trial, investigating the efficacy of remdesivir and hydroxychloroquine (HCQ). Thirty-eight percent had a CAT-score ≥ 10. DLCO was below the lower limit of normal in 29.6%. Ground-glass opacities were present in 39.8% on chest-CT, parenchymal bands were found in 41.7%. At admission, low pO2/FiO2 ratio, ICU treatment, high viral load, and low antibody levels, were predictors of a poorer pulmonary outcome after 3 months. High levels of matrix metalloproteinase (MMP)-9 during hospitalisation and at 3 months were associated with persistent CT-findings. Except for a negative effect of remdesivir on CAT-score, we found no effect of remdesivir or HCQ on long-term pulmonary outcomes. Three months after hospital admission for COVID-19, a high prevalence of respiratory symptoms, reduced DLCO, and persistent CT-findings was observed. Low pO2/FiO2 ratio, ICU-admission, high viral load, low antibody levels, and high levels of MMP-9 were associated with a worse pulmonary outcome.

Multi-state models are increasingly being used to model complex epidemiological and clinical outcomes over time. It is common to assume that the models are Markov, but the assumption can often be unrealistic. The Markov assumption is seldomly checked and violations can lead to biased estimation of many parameters of interest. This is a well known problem for the standard Aalen-Johansen estimator of transition probabilities and several alternative estimators, not relying on the Markov assumption, have been suggested. A particularly simple approach known as landmarking have resulted in the Landmark-Aalen-Johansen estimator. Since landmarking is a stratification method a disadvantage of landmarking is data reduction, leading to a loss of power. This is problematic for “less traveled” transitions, and undesirable when such transitions indeed exhibit Markov behaviour. Introducing the concept of partially non-Markov multi-state models, we suggest a hybrid landmark Aalen-Johansen estimator for transition probabilities. We also show how non-Markov transitions can be identified using a testing procedure. The proposed estimator is a compromise between regular Aalen-Johansen and landmark estimation, using transition specific landmarking, and can drastically improve statistical power. We show that the proposed estimator is consistent, but that the traditional variance estimator can underestimate the variance of both the hybrid and landmark estimator. Bootstrapping is therefore recommended. The methods are compared in a simulation study and in a real data application using registry data to model individual transitions for a birth cohort of 184 951 Norwegian men between states of sick leave, disability, education, work and unemployment.

Multi-state models are increasingly being used to model complex epidemiological and clinical outcomes over time. It is common to assume that the models are Markov, but the assumption can often be unrealistic. The Markov assumption is seldomly checked and violations can lead to biased estimation for many parameters of interest. As argued by Datta and Satten (2001), the Aalen-Johansen estimator of occupation probabilities is consistent also in the non-Markov case. Putter and Spitoni (2018) exploit this fact to construct a consistent estimator of state transition probabilities, the landmark Aalen-Johansen estimator, which does not rely on the Markov assumption. A disadvantage of landmarking is data reduction, leading to a loss of power. This is problematic for "less traveled" transitions, and undesirable when such transitions indeed exhibit Markov behaviour. Using a framework of partially non-Markov multi-state models we suggest a hybrid landmark Aalen-Johansen estimator for transition probabilities. The proposed estimator is a compromise between regular Aalen-Johansen and landmark estimation, using transition specific landmarking, and can drastically improve statistical power. The methods are compared in a simulation study and in a real data application modelling individual transitions between states of sick leave, disability, education, work and unemployment. In the application, a birth cohort of 184 951 Norwegian men are followed for 14 years from the year they turn 21, using data from national registries.

ObjectivesTo reduce sickness absence (SA) and increase work participation, the tripartite Agreement for a More Inclusive Working Life (IA) was established in Norway in 2001. IA companies have had access to several measures to prevent and reduce SA. Our aim in this paper was to estimate the average effect of having access to IA at the time of entering a first SA on later return-to-work (RTW) and on time spent in other work-related states. A secondary objective was to study how effects varied between women and men, and individuals with SA due to either musculoskeletal or psychological diagnoses.DesignPopulation-based observational multistate longitudinal cohort study.SettingIndividual characteristics and detailed longitudinal records of SA, work and education between 1997-2011 were obtained from population-wide registries.ParticipantsEach individual born in Norway 1967–1976 who entered full-time SA during 2004–2011, with limited earlier SA, was included (n=187 930).Primary and secondary outcome measuresIndividual multistate histories containing dated periods of work, graded SA, full-time SA, non-employment and education.MethodsData were analysed in a multistate model with 500 days of follow-up. The effect of IA was assessed by estimating differences in state probabilities over time, adjusted for confounders, using inverse probability weighting.ResultsIA increased the probability of work after SA, with the largest difference between groups after 29 days (3.4 percentage points higher (95% CI 2.5 to 4.3)). Differences in 1-year expected length of stay were 8.4 additional days (4.9 to 11.9) in work, 7.6 (4.8 to 10.3) fewer days in full-time SA and 1.6 (-0.2 to 3.4) fewer days in non-employment. Similar trends were found within subgroups by sex, musculoskeletal and psychological diagnoses. The robustness of the findings was studied in sensitivity analyses.ConclusionMeasures to prevent and reduce SA, as given through IA, were found to improve individuals’ RTW after entering SA.