Stacked probability plots of the extended illness-death model using constant transition hazards - an easy to use shiny app.

BACKGROUND: Extended illness-death models (a specific class of multistate models) are a useful tool to analyse situations like hospital-acquired infections, ventilation-associated pneumonia, and transfers between hospitals. The main components of these models are hazard rates and transition probabilities. Calculation of different measures and their interpretation can be challenging due to their complexity. METHODS: By assuming time-constant hazards, the complexity of these models becomes manageable and closed mathematical forms for transition probabilities can be derived. Using these forms, we created a tool in R to visualize transition probabilities via stacked probability plots. RESULTS: In this article, we present this tool and give some insights into its theoretical background. Using published examples, we give guidelines on how this tool can be used. Our goal is to provide an instrument that helps obtain a deeper understanding of a complex multistate setting. CONCLUSION: While multistate models (in particular extended illness-death models), can be highly complex, this tool can be used in studies to both understand assumptions, which have been made during planning and as a first step in analysing complex data structures. An online version of this tool can be found at https://eidm.imbi.uni-freiburg.de/ .

Protection from prior natural infection vs. vaccination against SARS-CoV-2-a statistical note to avoid biased interpretation.

Introduction: The fight against SARS-CoV-2 has been a major task worldwide since it was first identified in December 2019. An imperative preventive measure is the availability of efficacious vaccines while there is also a significant interest in the protective effect of a previous SARS-CoV-2 infection on a subsequent infection (natural protection rate). Methods: In order to compare protection rates after infection and vaccination, researchers consider different effect measures such as 1 minus hazard ratio, 1 minus odds ratio, or 1 minus risk ratio. These measures differ in a setting with competing risks. Nevertheless, as there is no unique definition, these metrics are frequently used in studies examining protection rate. Comparison of protection rates via vaccination and natural infection poses several challenges. For instance many publications consider the epidemiological definition, that a reinfection after a SARS-CoV-2 infection is only possible after 90 days, whereas there is no such constraint after vaccination. Furthermore, death is more prominent as a competing event during the first 90 days after infection compared to vaccination. In this work we discuss the statistical issues that arise when investigating protection rates comparing vaccination with infection. We explore different aspects of effect measures and provide insights drawn from different analyses, distinguishing between the first and the second 90 days post-infection or vaccination. Results: In this study, we have access to real-world data of almost two million people from Stockholm County, Sweden. For the main analysis, data of over 52.000 people is considered. The infected group is younger, includes more men, and is less morbid compared to the vaccinated group. After the first 90 days, these differences increased. Analysis of the second 90 days shows differences between analysis approaches and between age groups. There are age-related differences in mortality. Considering the outcome SARS-CoV-2 infection, the effect of vaccination versus infection varies by age, showing a disadvantage for the vaccinated in the younger population, while no significant difference was found in the elderly. Discussion: To compare the effects of immunization through infection or vaccination, we emphasize consideration of several investigations. It is crucial to examine two observation periods: The first and second 90-day intervals following infection or vaccination. Additionally, methods to address imbalances are essential and need to be used. This approach supports fair comparisons, allows for more comprehensive conclusions and helps prevent biased interpretations.

Lessons learned: avoiding bias via multi-state analysis of patients' trajectories in real-time.

Objectives: Many studies have attempted to determine the disease severity and patterns of COVID-19. However, at the beginning of the pandemic, the complex patients' trajectories were only descriptively reported, and many analyses were worryingly prone to time-dependent-, selection-, and competing risk biases. Multi-state models avoid these biases by jointly analysing multiple clinical outcomes while taking into account their time dependency, including current cases, and modelling competing events. This paper uses a publicly available data set from the first wave in Israel as an example to demonstrate the benefits of analysing hospital data via multi-state methodology. Methods: We compared the outcome of the data analysis using multi-state models with the outcome obtained when various forms of bias are ignored. Furthermore, we used Cox regression to model the transitions among the states in a multi-state model. This allowed for the comparison of the covariates' influence on transition rates between the two states. Lastly, we calculated expected lengths of stay and state probabilities based on the multi-state model and visualised it using stacked probability plots. Results: Compared to standard methods, multi-state models avoid many biases in the analysis of real-time disease developments. The utility of multi-state models is further highlighted through the use of stacked probability plots, which visualise the results. In addition, by stratification of disease patterns by subgroups and visualisation of the distribution of possible outcomes, these models bring the data into an interpretable form. Conclusion: To accurately guide the provision of medical resources, this paper recommends the real-time collection of hospital data and its analysis using multi-state models, as this method eliminates many potential biases. By applying multi-state models to real-time data, the gained knowledge allows rapid detection of altered disease courses when new variants arise, which is essential when informing medical and political decision-makers as well as the general population.

Assessing Marginal Effects of Non-Pharmaceutical Interventions on the Transmission of SARS-CoV-2 across Africa: A Hybrid Modeling Study.

Since 2019, a new strain of coronavirus has challenged global health systems. Due its fragile healthcare systems, Africa was predicted to be the most affected continent. However, past experiences of African countries with epidemics and other factors, including actions taken by governments, have contributed to reducing the spread of SARS-CoV-2. This study aims to assess the marginal impact of non-pharmaceutical interventions in fifteen African countries during the pre-vaccination period. To describe the transmission dynamics and control of SARS-CoV-2 spread, an extended time-dependent SEIR model was used. The transmission rate of each infectious stage was obtained using a logistic model with NPI intensity as a covariate. The results revealed that the effects of NPIs varied between countries. Overall, restrictive measures related to assembly had, in most countries, the largest reducing effects on the pre-symptomatic and mild transmission, while the transmission by severe individuals is influenced by privacy measures (more than 10%). Countries should develop efficient alternatives to assembly restrictions to preserve the economic sector. This involves, e.g. training in digital tools and strengthening digital infrastructures.

Methodological biases in observational hospital studies of COVID-19 treatment effectiveness: pitfalls and potential.

Introduction: This study aims to discuss and assess the impact of three prevalent methodological biases: competing risks, immortal-time bias, and confounding bias in real-world observational studies evaluating treatment effectiveness. We use a demonstrative observational data example of COVID-19 patients to assess the impact of these biases and propose potential solutions. Methods: We describe competing risks, immortal-time bias, and time-fixed confounding bias by evaluating treatment effectiveness in hospitalized patients with COVID-19. For our demonstrative analysis, we use observational data from the registry of patients with COVID-19 who were admitted to the Bellvitge University Hospital in Spain from March 2020 to February 2021 and met our predefined inclusion criteria. We compare estimates of a single-dose, time-dependent treatment with the standard of care. We analyze the treatment effectiveness using common statistical approaches, either by ignoring or only partially accounting for the methodological biases. To address these challenges, we emulate a target trial through the clone-censor-weight approach. Results: Overlooking competing risk bias and employing the naïve Kaplan-Meier estimator led to increased in-hospital death probabilities in patients with COVID-19. Specifically, in the treatment effectiveness analysis, the Kaplan-Meier estimator resulted in an in-hospital mortality of 45.6% for treated patients and 59.0% for untreated patients. In contrast, employing an emulated trial framework with the weighted Aalen-Johansen estimator, we observed that in-hospital death probabilities were reduced to 27.9% in the "X"-treated arm and 40.1% in the non-"X"-treated arm. Immortal-time bias led to an underestimated hazard ratio of treatment. Conclusion: Overlooking competing risks, immortal-time bias, and confounding bias leads to shifted estimates of treatment effects. Applying the naïve Kaplan-Meier method resulted in the most biased results and overestimated probabilities for the primary outcome in analyses of hospital data from COVID-19 patients. This overestimation could mislead clinical decision-making. Both immortal-time bias and confounding bias must be addressed in assessments of treatment effectiveness. The trial emulation framework offers a potential solution to address all three methodological biases.

Association of Staphylococcus aureus Bacterial Load and Colonization Sites With the Risk of Postoperative S. aureus Infection.

Background: The independent effects of extranasal-only carriage, carriage at multiple bodily sites, or the bacterial load of colonizing Staphylococcus aureus (SA) on the risk of developing SA surgical site infections and postoperative bloodstream infections (SA SSI/BSIs) are unclear. We aimed to quantify these effects in this large prospective cohort study. Methods: Surgical patients aged 18 years or older were screened for SA carriage in the nose, throat, or perineum within 30 days before surgery. SA carriers and noncarriers were enrolled in a prospective cohort study in a 2:1 ratio. Weighted multivariable Cox proportional hazard models were used to assess the independent associations between different measures of SA carriage and occurrence of SA SSI/BSI within 90 days after surgery. Results: We enrolled 5004 patients in the study cohort; 3369 (67.3%) were SA carriers. 100 SA SSI/BSI events occurred during follow-up, and 86 (86%) of these events occurred in SA carriers. The number of colonized bodily sites (adjusted hazard ratio [aHR], 3.5-8.5) and an increasing SA bacterial load in the nose (aHR, 1.8-3.4) were associated with increased SA SSI/BSI risk. However, extranasal-only carriage was not independently associated with SA SSI/BSI (aHR, 1.5; 95% CI, 0.9-2.5). Conclusions: Nasal SA carriage was associated with an increased risk of SA SSI/BSI and accounted for the majority of SA infections. Higher bacterial load, as well as SA colonization at multiple bodily sites, further increased this risk.

Diagnostic evaluation of paediatric autoimmune lymphoproliferative immunodeficiencies (ALPID): a prospective cohort study.

BACKGROUND: Lymphoproliferation and autoimmune cytopenias characterise autoimmune lymphoproliferative syndrome. Other conditions sharing these manifestations have been termed autoimmune lymphoproliferative syndrome-like diseases, although they are frequently more severe. The aim of this study was to define the genetic, clinical, and immunological features of these disorders to improve their diagnostic classification. METHODS: In this prospective cohort study, patients were referred to the Center for Chronic Immunodeficiency in Freiburg, Germany, between Jan 1, 2008 and March 5, 2022. We enrolled patients younger than 18 years with lymphoproliferation and autoimmune cytopenia, lymphoproliferation and at least one additional sign of an inborn error of immunity (SoIEI), bilineage autoimmune cytopenia, or autoimmune cytopenia and at least one additional SoIEI. Autoimmune lymphoproliferative syndrome biomarkers were determined in all patients. Sanger sequencing followed by in-depth genetic studies were recommended for patients with biomarkers indicative of autoimmune lymphoproliferative syndrome, while IEI panels, exome sequencing, or genome sequencing were recommended for patients without such biomarkers. Genetic analyses were done as decided by the treating physician. The study was registered on the German Clinical Trials Register, DRKS00011383, and is ongoing. FINDINGS: We recruited 431 children referred for autoimmune lymphoproliferative syndrome evaluation, of whom 236 (55%) were included on the basis of lymphoproliferation and autoimmune cytopenia, 148 (34%) on the basis of lymphoproliferation and another SoIEI, 33 (8%) on the basis of autoimmune bicytopenia, and 14 (3%) on the basis of autoimmune cytopenia and another SoIEI. Median age at diagnostic evaluation was 9·8 years (IQR 5·5-13·8), and the cohort comprised 279 (65%) boys and 152 (35%) girls. After biomarker and genetic assessments, autoimmune lymphoproliferative syndrome was diagnosed in 71 (16%) patients. Among the remaining 360 patients, 54 (15%) had mostly autosomal-dominant autoimmune lymphoproliferative immunodeficiencies (AD-ALPID), most commonly affecting JAK-STAT (26 patients), CTLA4-LRBA (14), PI3K (six), RAS (five), or NFκB (three) signalling. 19 (5%) patients had other IEIs, 17 (5%) had non-IEI diagnoses, 79 (22%) were unresolved despite extended genetics (ALPID-U), and 191 (53%) had insufficient genetic workup for diagnosis. 16 (10%) of 161 patients with a final diagnosis had somatic mutations. Alternative classification of patients fulfilling common variable immunodeficiency or Evans syndrome criteria did not increase the proportion of genetic diagnoses. INTERPRETATION: The ALPID phenotype defined in this study is enriched for patients with genetic diseases treatable with targeted therapies. The term ALPID might be useful to focus diagnostic and therapeutic efforts by triggering extended genetic analysis and consideration of targeted therapies, including in some children currently classified as having common variable immunodeficiency or Evans syndrome. FUNDING: Deutsche Forschungsgemeinschaft under Germany's Excellence Strategy. TRANSLATION: For the German translation of the abstract see Supplementary Materials section.