1,721,067 research outputs found
Geospatial patterns of excess mortality in Belgium: Insights from the first year of the COVID-19 pandemic
No full textObjectives: Belgium experienced multiple COVID-19 waves that hit various groups in the population, which changed the mortality pattern compared to periods before the pandemic. In this study, we investigated the geographical excess mortality trend in Belgium during the first year of the COVID-19 pandemic. Methods: We retrieved the number of deaths and population data in 2020 based on gender, age, and municipality of residence, and we made a comparison with the mortality data in 2017–2019 using a spatially discrete model. Results: Excess mortality was significantly associated with age, gender, and COVID-19 incidence, with larger effects in the second half of 2020. Most municipalities had higher risks of mortality with a number of exceptions in the northeastern part of Belgium. Some discrepancies in excess mortality were observed between the north and south regions. Conclusions: This study offers useful insight into excess mortality and will aid local and regional authorities in monitoring mortality trends.Funding T.N. gratefully acknowledges funding by the Internal Funds KU Leuven, Belgium (project number 3M190682) and the Fund for Scientific Research– Flanders, Belgium (grant number G0A4121N). C.F. acknowledges support from the European Union’s Horizon 2020project EpiPose (Grant agreement number 101003688) and European Union’s Horizon Europe– project ESCAPE (Grant agreement number 101095619). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency (HADEA). Neither the European Union nor the granting authority can be held responsible for them
When and where? Day-night alterations in wild boar space use captured by a generalized additive mixed model
No full textWild boar (Sus scrofa), an abundant species across Europe, is often subjected to management in agro-ecosystems in order to control population size, or to scare them away from agricultural fields to safeguard crop yields. Wild boar management can benefit from a better understanding on changes in its space use across the diel cycle (i.e., diel space use) in relation to variable hunting pressures or other factors. Here, we estimate wild boar diel space use in an agro-ecosystem in central Belgium during four consecutive "growing seasons"(i.e., April-September). To achieve this, we fit generalized additive mixed models (GAMMs) to camera trap data of wild boar aggregated over 1-h periods. Our results reveal that wild boar are predominantly nocturnal in all of the hunting management zones in Meerdaal, with activity peaks around sunrise and sunset. Hunting events in our study area tend to take place around sunrise and sunset, while non -lethal human activities occur during sunlight hours. Our GAMM reveals that wild boar use different areas throughout the diel cycle. During the day, wild boar utilized areas in the centre of the forest, possibly to avoid human activities during daytime. During the night, they foraged near (or in) agricultural fields. A post hoc comparison of space use maps of wild boar in Meerdaal revealed that their diurnal and nocturnal space use were uncorrelated. We did not find sufficient evidence to prove that wild boar spatiotemporally avoid hunters. Finally, our work reveals the potential of GAMMs to model variation in space across 24-h periods from camera trap data, an application that will be useful to address a range of ecological questions. However, to test the robustness of this approach we advise that it should be compared against telemetry -based methods to derive diel space use.Funding
This work makes use of data and/or infrastructure provided by INBO and funded by Research Foundation-Flanders (FWO) as part of the Belgian contribution to LifeWatch. Martijn Bollen is a PhD fellow funded by a BOF mandate at Hasselt University. Thomas Neyens received funding from the FWO (G0A4121N) and from the Internal Funds KU Leuven (project number 3M190682). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
ACKNOWLEDGEMENTS
We are grateful to the Flemish Agency for Nature and Forest and the local nature conservation NGO ‘‘Vrienden van Heverleebos en Meerdaalwoud’’ to allow us to place camera traps on their properties. Further, we thank all volunteers and students that aided in the field or processed and annotated photographs. Our final word of gratitude goes to Donald Kramer, Oliver Keuling and Frederik Dalerum for providing us with valuable feedback, which has improved both the form and content of this article
Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium
No full textIntroduction: COVID-19 remains a major concern globally. Therefore, it is important to evaluate COVID-19's rapidly changing trends. The fractal dimension has been proposed as a viable method to characterize COVID-19 curves since epidemic data is often subject to considerable heterogeneity. In this study, we aim to investigate the association between various socio-demographic factors and the complexity of the COVID-19 curve as quantified through its fractal dimension.
Methods: We collected population indicators data (ethnic composition, socioeconomic status, number of inhabitants, population density, the older adult population proportion, vaccination rate, satisfaction, and trust in the government) at the level of the statistical sector in Belgium. We compared these data with fractal dimension indicators of COVID-19 incidence between 1 January – 31 December 2021 using canonical correlation analysis.
Results: Our results showed that these population indicators have a significant association with COVID-19 incidences, with the highest explanatory and predictive power coming from the number of inhabitants, population density, and ethnic composition.
Conclusion: It is important to monitor these population indicators during a pandemic, especially when dealing with targeted interventions for a specific population.The authors thank Pieter Chys and Benoit Turbang for providing COVID-19 daily cases and vaccination data in the Flemish region. The authors also thank Jasper Sans for providing COVID-19 vaccination data for Brussels. TN and CF gratefully acknowledge funding by the Fund for Scientific Research— Flanders (grant number 3G0G9820). The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication
Unraveling the impact of the COVID-19 pandemic on the mortality trends in Belgium between 2020-2022
No full textBackgroundOver the past four years, the COVID-19 pandemic has exerted a profound impact on public health, including on mortality trends. This study investigates mortality patterns in Belgium by examining all-cause mortality, excess mortality, and cause-specific mortality. MethodsWe retrieved all-cause mortality data from January 1, 2009, to December 31, 2022, stratified by age group and sex. A linear mixed model, informed by all-cause mortality from 2009 to 2019, was used to predict non-pandemic all-cause mortality rates in 2020-2022 and estimate excess mortality. Further, we also analyzed trends in cause-specific and premature mortality. ResultsDifferent all-cause mortality patterns could be observed between the younger (<45 years) and older age groups. The impact of the COVID-19 pandemic was particularly evident among older age groups. The highest excess mortality occurred in 2020, while a reversal in this trend was evident in 2022. We observed a notable effect of COVID-19 on cause-specific and premature mortality patterns over the three-year period. ConclusionsDespite a consistent decline in COVID-19 reported mortality over this three-year period, it remains imperative to meticulously monitor mortality trends in the years ahead.The authors declare that no funds, grants, or other support were received during the preparation of this manuscript
Simulation-based assessment of the performance of hierarchical abundance estimators for camera trap surveys of unmarked species
No full textKnowledge on animal abundances is essential in ecology, but is complicated by low detectability of many species. This has led to a widespread use of hierarchical models (HMs) for species abundance, which are also commonly applied in the context of nature areas studied by camera traps (CTs). However, the best choice among these models is unclear, particularly based on how they perform in the face of complicating features of realistic populations, including: movements relative to sites, multiple detections of unmarked individuals within a single survey, and low detectability. We conducted a simulation-based comparison of three HMs (Royle-Nichols, binomial N-mixture and Poisson N-mixture model) by generating groups of unmarked individuals moving according to a bivariate Ornstein-Uhlenbeck process, monitored by CTs. Under a range of simulated scenarios, none of the HMs consistently yielded accurate abundances. Yet, the Poisson N-mixture model performed well when animals did move across sites, despite accidental double counting of individuals. Absolute abundances were better captured by Royle-Nichols and Poisson N-mixture models, while a binomial N-mixture model better estimated the actual number of individuals that used a site. The best performance of all HMs was observed when estimating relative trends in abundance, which were captured with similar accuracy across these models.MB is a PhD fellow funded by a BOF mandate at Hasselt University. TN gratefully acknowledges the Research Foundation – Flanders [Grant Number G0A4121N] and by the Internal Funds KU Leuven [project number
3M190682]. Te resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation—Flanders (FWO) and the Flemish Government.
We would like to express our gratitude towards Marc Kéry for his feedback on an early draf of our work. Furthermore, we are grateful for the valuable comments of two anonymous reviewers. Together, their feedback has
improved the clarity and quality of the pape
Assessing the impact of neighborhood structures in Bayesian disease mapping
No full textIn Bayesian disease mapping, defining the neighborhood structure is crucial when fitting the conditional auto-regressive model. Yet, there has been little assessment of how different structures affect the model performance in case of fine-scale data. This paper explores this gap. In a case study examining COVID-19 pandemic effects, 2020 mortality is contrasted with pre-pandemic rates in small areas in Limburg (Belgium). Data are modeled using BYM and BYM2, with three broadening queen-neighborhood structures up to the fifth-order neighbors and two weight schemes. A simulation study assesses model performance in reproducing the pairwise spatial correlation at different neighbor orders. Models are compared regarding WAIC, goodness-of-fit, parameter estimates, and computation time. Results show that the order-based weight matrix performs better than the binary matrix. The simple first-order neighborhood structure shows comparable performance to larger higher-order structures while requiring much less computation time. The BYM model is more impacted by the choice of the neighborhood as compared to the BYM2 model. Our findings suggest minimal advantages in employing higher-order neighborhood matrices. In conclusion, our study indicates that opting for a simple first-order neighborhood structure is a pragmatic and suitable choice when applying a conditional auto-regressive model to fine-scale data in Bayesian disease mapping.TN gratefully acknowledges financial support from the Research Foundation - Flanders [grant number G0A4121N]
Model-based disease mapping using primary care registry data
No full textBackground: Spatial modeling of disease risk using primary care registry data is promising for public health surveillance. However, it remains unclear to which extent challenges such as spatially disproportionate sampling and practice-specific reporting variation affect statistical inference. Methods: Using lower respiratory tract infection data from the INTEGO registry, modeled with a logistic model incorporating patient characteristics, a spatially structured random effect at municipality level, and an unstructured random effect at practice level, we conducted a case and simulation study to assess the impact of these challenges on spatial trend estimation. Results: Even with spatial imbalance and practice-specific reporting variation, the model performed well. Performance improved with increasing spatial sample balance and decreasing practice-specific variation. Conclusion: Our findings indicate that, with correction for reporting efforts, primary care registries are valuable for spatial trend estimation. The diversity of patient locations within practice populations plays an important role.Funding statement
INTEGO is funded regularly by the Flemish Government (Ministry of Health and Welfare). TN gratefully acknowledges funding by the Internal Funds KU Leuven (project number 3M190682). PJKL acknowledges support from the Research Foundation Flanders (FWO, fwo.be) (postdoctoral fellowship 1242021N) and the Research council of the Vrije Universiteit Brussel (OZR-VUB) via grant number OZR3863BOF.
Acknowledgments
We thank the VSC (Flemish Supercomputer Center), funded by the Research Foundation Flanders (FWO) and the Flemish Government, which provided the resources and services used to perform the simulations in this work
The (in)stability of Bayesian model selection criteria in disease mapping
No full textSeveral model comparison techniques exist to select the best fitting model from a set of candidate models. This study explores the performance of model comparison tools that are commonly used in Bayesian spatial disease mapping and that are available among several Bayesian software packages: the deviance information criterion (DIC), the Watanabe–Akaike information criterion (WAIC) and the log marginal predictive likelihood (LMPL). We compare R packages CARBayes and NIMBLE, and R interfaces to OpenBUGS (R2OpenBUGS) and Stan (RStan), by fitting Poisson models to disease incidence outcomes with intrinsic conditional autoregressive, convolution conditional autoregressive and log-normal error terms. From three data analyses that differ in the number of areal units and background incidence/prevalence of the outcome of interest, we learn that the estimates of model comparison statistics coming from different software packages can lead to disagreements regarding model preference. Furthermore, we show that the distributional convergence of parameter estimates does not necessarily imply numerical convergence of the model comparison tool. We warn users to be careful when doing model comparison when using different software packages, and to make use of one specific method for the calculation of the model selection criteria.During the completion of this study, Neyens T. was funded as a postdoctoral researcher by the FWO flanders, Belgium (grant number 12S7217N)
Integrated Nested Laplace Approximation as a new approximation method for the combined model: A simulation study
No full text© 2017, © 2017 Taylor & Francis Group, LLC. The combined model accounts for different forms of extra-variability and has traditionally been applied in the likelihood framework, or in the Bayesian setting via Markov chain Monte Carlo. In this article, integrated nested Laplace approximation is investigated as an alternative estimation method for the combined model for count data, and compared with the former estimation techniques. Longitudinal, spatial, and multi-hierarchical data scenarios are investigated in three case studies as well as a simulation study. As a conclusion, integrated nested Laplace approximation provides fast and precise estimation, while avoiding convergence problems often seen when using Markov chain Monte Carlo.sponsorship: Financial support from the IAP research network #P7/06 of the Belgian Government (Belgian Science Policy) and the Research Foundation Flanders (12S7217N) is gratefully acknowledged. (IAP research network of the Belgian Government (Belgian Science Policy)|P7/06, Research Foundation Flanders|12S7217N)status: Published onlin
A Comparison of Items and Constructs of Standardized Health-Related Quality of Life and Mental Well-Being Measures
No full textObjectives: This study aimed to explore the internal constructs of the concepts being measured by EQ-5D-5L (a health-related quality of life measure that can produce preference-based utility values) and the 12-item General Health Questionnaire (GHQ12, a mental well-being measure) and to understand to what extent the items of EQ-5D-5L and GHQ-12 associate with each other. Methods: We used data from 12 701 respondents participating in a Belgian survey in 2022. Correlation coefficients between GHQ-12 and EQ-5D-5L were calculated at both the aggregate and item levels. Multidimensional scaling, exploratory factor analysis, and regression models were performed to investigate the underlying constructs that are associated with the items. Results: Despite a moderate correlation (0.39) between the EQ-5D-5L and GHQ-12 total scores, only a trivial or weak correlation (,0.3) was observed between the first 4 EQ-5D-5L items and any GHQ-12 item. Multidimensional scaling and exploratory factor analysis showed the first 4 EQ-5D-5L dimensions were clustered together with EuroQol visual analog scale and positively phrased GHQ-12 items were close to each other, whereas EQ-anxiety/depression and negatively phrased GHQ-12 items were grouped with overall life satisfaction. In the regression models, not all GHQ-12 items had a significant coefficient to predict EQ-5D-5L responses. Conclusions: To the best of our knowledge, we present the first comparison of items and underlying constructs of GHQ-12 and EQ-5D-5L. The results showed that GHQ-12 can only partially predict the responses of EQ-5D-5L and the 2 instruments measure different constructs. Researchers should carefully consider conceptual legitimacy while applying the mapping technique and consider sensitivity analyses for the mapping estimates.Funding/Support:
This work was cofunded by the Research Foundation Flanders (FWO Grant G0G1920N, 2020), the University of Antwerp Research Fund, the Epipose (101003688) and ESCAPE (101095619) projects, of the European Union Horizon 2020 and Horizon Europe research and innovation program, and the EuroQol Research Foundation (EQ Project 1588-RA). Dr. Neyens acknowledges additional personal funding from FWO (3G0G9820)
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