1,721,920 research outputs found
Determinants of COVID-19 vaccination coverage in European and Organisation for Economic Co-operation and Development (OECD) countries
No full textIntroduction In relatively wealthy countries, substantial between-country variability in COVID-19 vaccination coverage occurred. We aimed to identify influential national-level determinants of COVID-19 vaccine uptake at different COVID-19 pandemic stages in such countries.Methods We considered over 50 macro-level demographic, healthcare resource, disease burden, political, socio-economic, labor, cultural, life-style indicators as explanatory factors and coverage with at least one dose by June 2021, completed initial vaccination protocols by December 2021, and booster doses by June 2022 as outcomes. Overall, we included 61 European or Organisation for Economic Co-operation and Development (OECD) countries. We performed 100 multiple imputations correcting for missing data and partial least squares regression for each imputed dataset. Regression estimates for the original covariates were pooled over the 100 results obtained for each outcome. Specific analyses focusing only on European Union (EU) or OECD countries were also conducted.Results Higher stringency of countermeasures, and proportionately more older adults, female and urban area residents, were each strongly and consistently associated with higher vaccination rates. Surprisingly, socio-economic indicators such as gross domestic product (GDP), democracy, and education had limited explanatory power. Overall and in the OECD, greater perceived corruption related strongly to lower vaccine uptake. In the OECD, social media played a noticeable positive role. In the EU, right-wing government ideology exhibited a consistently negative association, while cultural differences had strong overall influence.Conclusion Relationships between country-level factors and COVID-19 vaccination uptake depended on immunization stage and country reference group. Important determinants include stringency, population age, gender and urbanization, corruption, government ideology and cultural context.The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study received funding from the Research Foundation Flanders (FWO project number G0D5917N) and the European Union’s Horizon 2020 research and innovation programme (Project EpiPose – project number 101003688, 2020 and Project ESCAPE – project number 101095619). The sponsors had no role in the study design;
in the collection, analysis and interpretation of data; in writing the article; and in the decision to submit it for publication. This work reflects only the authors’ views. The European Commission is not responsible for any use that may be made of the information it contains
On the Addams family of discrete frailty distributions for modeling multivariate case I interval-censored data
No full textAbstract Random effect models for time-to-event data, also known as frailty models, provide a conceptually appealing way of quantifying association between survival times and of representing heterogeneities resulting from factors which may be difficult or impossible to measure. In the literature, the random effect is usually assumed to have a continuous distribution. However, in some areas of application, discrete frailty distributions may be more appropriate. The present paper is about the implementation and interpretation of the Addams family of discrete frailty distributions. We propose methods of estimation for this family of densities in the context of shared frailty models for the hazard rates for case I interval-censored data. Our optimization framework allows for stratification of random effect distributions by covariates. We highlight interpretational advantages of the Addams family of discrete frailty distributions and theK-point distribution as compared to other frailty distributions. A unique feature of the Addams family and the K-point distribution is that the support of the frailty distribution depends on its parameters. This feature is best exploited by imposing a model on the distributional parameters, resulting in a model with non-homogeneous covariate effects that can be analyzed using standard measures such as the hazard ratio. Our methods are illustrated with applications to multivariate case I interval-censored infection data
Rejoinder to Discussion of "A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks''
Full text linkThis rejoinder responds to discussions by of Caimo, Niezink, and
Schweinberger and Fritz of ''A Tale of Two Datasets: Representativeness and
Generalisability of Inference for Samples of Networks'' by Krivitsky, Coletti,
and Hens, all published in the Journal of the American Statistical Association
in 2023.Comment: 10 pages, 3 figures, 3 table
A Bayesian mixture model accounting for individual heterogeneity in response to pathogenic infection
Full text linkThe analysis of multivariate serological data derived from blood serum samples and tested for the presence of antibodies against multiple pathogens gained attention in recent years. Despite the common use of a so-called threshold approach to classify individuals as seronegative or -positive, limitations of such an approach have been reported in the literature, with the subjective choice of the threshold being the most important. Here, we consider a Bayesian mixture approach to model continuous IgG antibody concentrations directly while accounting for the presence of individual heterogeneity and implied association between antibody titer levels for two infections. We fitted the proposed model to Belgian bivariate serological data on the varicella-zoster virus (VZV) and parvovirus B19 (PVB19). Given the existing body of evidence with respect to possible reinfections with PVB19, we investigated whether models explicitly accounting for waning of humoral immunity improved model fit. Our results showed that although after a steep rise with age, the observed seroprevalence for PVB19 decreases between the ages of 20 and 40, the mean IgG antibody concentration remains constant with age among individuals in the seropositive component. This could provide evidence of a direct impact of reinfections with PVB19 on the observed IgG antibody levels, while individuals with loss of humoral immunity after natural infection imply an increase in susceptibility. For VZV, the mean IgG antibody levels slightly decrease with increasing age among seropositive individuals, indicating only very limited waning of humoral immunity as age-dependent seroprevalence estimates are monotonically increasing with increasing age. In general, based on our analyses, we showed that mixture models provide additional insights concerning the waning of humoral immunity as compared to more traditional frailty approaches, which focus on estimating the seroprevalence solely while the model is sufficiently flexible to capture observed dynamics in IgG antibody decay
Nonparametric serial interval estimation with uniform mixtures
No full textThe serial interval of an infectious disease is a key instrument to understand transmission dynamics. Estimation of the serial interval distribution from illness onset data extracted from transmission pairs is challenging due to the presence of censoring and state-of-the-art methods mostly rely on parametric models. We present a fully data-driven methodology to estimate the serial interval distribution based on interval-censored serial interval data. The proposed nonparametric estimator of the cumulative distribution function of the serial interval is based on the class of uniform mixtures. Closed-form solutions are available for point estimates of different serial interval features and the bootstrap is used to construct confidence intervals. Algorithms underlying our approach are simple, stable, and computationally inexpensive, making them easily implementable in a programming language that is most familiar to a potential user. The nonparametric user-friendly routine is included in the EpiDelays package for ease of implementation. Our method complements existing parametric approaches for serial interval estimation and permits to analyze past, current, or future illness onset data streams following a set of best practices in epidemiological delay modeling.OG and NH were supported by the VERDI project (101045989) and the ESCAPE project (101095619), funded by the European Union. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or European Health and Digital Executive Agency (HADEA). Neither the European Union nor the granting authority can be held responsible for them. OG and NH acknowledge the financial support of the Fondation Universitaire de Belgique (file nr. AS-0608). OG and NH were also supported by the BE-PIN project (contract nr. TD/231/BE-PIN) funded by BELSPO (Belgian Science Policy Office) as part of the POST-COVID programme. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
The impact of behavioral interventions on co-infection dynamics: An exploration of the effects of home isolation
No full textBehavioral epidemiology, the field aiming to determine the impact of individual behavior on the spread of an epidemic, has gained increased recognition during the last few decades. Behavioral changes due to the development of symptoms have been studied in mono-infections. However, in reality, multiple infections are circulating within the same time period and behavioral changes resulting from contraction of one of the diseases affect the dynamics of the other. The present study aims at assessing the effect of home isolation on the joint dynamics of two infectious diseases, including co-infection, assuming that the two diseases do not confer cross-immunity. We use an age- and time- structured co-infection model based on partial differential equations. Social contact matrices, describing different mixing patterns of symptomatic and asymptomatic individuals are incorporated into the calculation of the age- and time-specific marginal forces of infection. Two scenarios are simulated, assuming that one of the diseases has more severe symptoms than the other. In the first scenario, people stay only at home when having symptoms of the most severe disease. In the second scenario, twice as many people stay at home when having symptoms of the most severe disease than when having symptoms of the other disease. The results show that the impact of home isolation on the joint dynamics of two infectious diseases depends on the epidemiological parameters and properties of the diseases (e.g., basic reproduction number, symptom severity). In case both diseases have a low to moderate basic reproduction number, and there is no home isolation for the less severe disease, the final size of the less severe disease increases with the proportion of symptomatic cases of the most severe disease staying at home, after an initial decrease. This counterintuitive result could be explained by a shift in the peak time of infection of the disease with the most severe symptoms, resulting in a smaller number of people with less contacts at the peak time of the other infection. When twice as many people stay at home when having symptoms of the most severe disease than when having symptoms of the other disease, increasing the proportion staying at home always reduces the final size of both diseases, and the number of co-infections. In conclusion, when providing advise if people should stay at home in the context of two or more co-circulating diseases, one has to take into account epidemiological parameters and symptom severity. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)This research is part of a project that has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement 682540 TransMID). NH gratefully acknowledges support from the University of Antwerp scientific chair in Evidence-Based Vaccinology, financed in 2009-2017 by a gift from Pfizer and in 2016 by a gift from GSK. We gratefully acknowledge Thomas Kovac (UHasselt) for improving our R code for running the model. We thank Kim Van Kerckhove (UHasselt, Ugentec) and Eva Santermans (UHasselt, Galapagos) for their input and discussions on social contact data. We thank James Wood (UNSW Sidney, Australia) for his helpful comments and discussions that improved the manuscript. We like to thank the reviewers for the constructive comments
Correction: A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks
No full textThis note provides correction to some numerical results in Krivitsky P. N., Coletti, P., and Hens, N. (2023), "A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks," Journal of the American Statistical Association, 118, 2213-2224
Optimising the case-crossover design for use in shared exposure settings
No full textAbstract: With a case-crossover design, a case's exposure during a risk period is compared to the case's exposures at referent periods. The selection of referents for this self-controlled design is determined by the referent selection strategy (RSS). Previous research mainly focused on systematic bias associated with the RSS. We additionally focused on how RSS determines the number of referents per risk, sensitivity to overdispersion and time-varying confounding. We illustrated the consequences of different RSS using a simulation study informed by data on meteorological variables and Legionnaires' disease. By randomising the events and exposure time series, we explored statistical power associated with time-stratified and fixed bidirectional RSS and their susceptibility to systematic bias and confounding bias. In addition, we investigated how a high number of events on the same date (e.g. outbreaks) affected coefficient estimation. As illustrated by our work, referent selection alone can be insufficient to control for a time-varying confounding bias. In contrast to systematic bias, confounding bias can be hard to detect. We studied potential solutions: varying the model parameters and link-function, outlier-removal and aggregating the input-data over smaller areas. Our simulation study offers a framework for researchers looking to detect and to avoid bias in case-crossover studies
Bayesian Nowcasting with Laplacian-P-Splines
No full textDuring an epidemic, the daily number of reported infected cases, deaths or hospitalizations is often lower than the actual number due to reporting delays. Nowcasting aims to estimate the cases that have not yet been reported and combine it with the already reported cases to obtain an estimate of the daily cases. In this article, we present a fast and flexible Bayesian approach for nowcasting by combining P-splines and Laplace approximations. Laplacian-P-splines provide a flexible framework for nowcasting that is computationally less demanding as compared to traditional Markov chain Monte Carlo techniques. The proposed approach also permits to naturally quantify the prediction uncertainty. Model performance is assessed through simulations and the nowcasting method is applied to COVID-19 mortality and incidence cases in Belgium. Supplementary materials for this article are available online.VERDI: This project was supported by the VERDI project (101045989), funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the Health and Digital Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.
ESCAPE: This project was supported by the ESCAPE project (101095619), funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Health and Digital Executive Agency (HADEA). Neither the European Union nor the granting authority can be held responsible for them. The authors acknowledge funding from the Special Research Fund through the Methusalem project BOF22M01
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