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Petrof O, Neyens T, Vaes B, Janssens A, Faes C. Using a general practice research database to assess the spatio-temporal COVID-19 risk. BMC PRIMARY CARE 2024; 25:175. [PMID: 38773431 PMCID: PMC11106891 DOI: 10.1186/s12875-024-02423-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 05/08/2024] [Indexed: 05/23/2024]
Abstract
BACKGROUND In Flanders, general practitioners (GPs) were among the first ones to collect data regarding COVID-19 cases. Intego is a GPs' morbidity registry in primary care with data collected from the electronic medical records from a sample of general practices. The Intego database contain elaborate information regarding patient characteristics, such as comorbidities. At the national level, the Belgian Public Health Institute (Sciensano) recorded all test-confirmed COVID-19 cases, but without other patient characteristics. METHODS Spatio and spatio-temporal analyses were used to analyse the spread of COVID-19 incidence at two levels of spatial aggregation: the municipality and the health sector levels. Our study goal was to compare spatio-temporal modelling results based on the Intego and Sciensano data, in order to see whether the Intego database is capable of detecting epidemiological trends similar to those in the Sciensano data. Comparable results would allow researchers to use these Intego data, and their wealth of patient information, to model COVID-19-related processes. RESULTS The two data sources provided comparable results. Being a male decreased the odds of having COVID-19 disease. The odds for the age categories (17,35], (35,65] and (65,110] of being a confirmed COVID-19 case were significantly higher than the odds for the age category [0,17]. In the Intego data, having one of the following comorbidities, i.e., chronic kidney disease, heart and vascular disease, and diabetes, was significantly associated with being a COVID-19 case, increasing the odds of being diagnosed with COVID-19. CONCLUSION We were able to show how an alternative data source, the Intego data, can be used in a pandemic situation. We consider our findings useful for public health officials who plan intervention strategies aimed at monitor and control disease outbreaks such as that of COVID-19.
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Affiliation(s)
- Oana Petrof
- I-Biostat, Hasselt University, Diepenbeek, Belgium.
| | - Thomas Neyens
- I-Biostat, Hasselt University, Diepenbeek, Belgium
- Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat), KU Leuven, Leuven, Belgium
| | - Bert Vaes
- Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | - Arne Janssens
- Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
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Manz KM, Schwettmann L, Mansmann U, Maier W. Area Deprivation and COVID-19 Incidence and Mortality in Bavaria, Germany: A Bayesian Geographical Analysis. Front Public Health 2022; 10:927658. [PMID: 35910894 PMCID: PMC9334899 DOI: 10.3389/fpubh.2022.927658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 06/23/2022] [Indexed: 11/13/2022] Open
Abstract
Background Area deprivation has been shown to be associated with various adverse health outcomes including communicable as well as non-communicable diseases. Our objective was to assess potential associations between area deprivation and COVID-19 standardized incidence and mortality ratios in Bavaria over a period of nearly 2 years. Bavaria is the federal state with the highest infection dynamics in Germany and demographically comparable to several other European countries. Methods In this retrospective, observational ecological study, we estimated the strength of associations between area deprivation and standardized COVID-19 incidence and mortality ratios (SIR and SMR) in Bavaria, Germany. We used official SARS-CoV-2 reporting data aggregated in monthly periods between March 1, 2020 and December 31, 2021. Area deprivation was assessed using the quintiles of the 2015 version of the Bavarian Index of Multiple Deprivation (BIMD 2015) at district level, analyzing the overall index as well as its single domains. Results Deprived districts showed higher SIR and SMR than less deprived districts. Aggregated over the whole period, the SIR increased by 1.04 (95% confidence interval (95% CI): 1.01 to 1.07, p = 0.002), and the SMR by 1.11 (95% CI: 1.07 to 1.16, p < 0.001) per BIMD quintile. This represents a maximum difference of 41% between districts in the most and least deprived quintiles in the SIR and 110% in the SMR. Looking at individual months revealed clear linear association between the BIMD quintiles and the SIR and SMR in the first, second and last quarter of 2021. In the summers of 2020 and 2021, infection activity was low. Conclusions In more deprived areas in Bavaria, Germany, higher incidence and mortality ratios were observed during the COVID-19 pandemic with particularly strong associations during infection waves 3 and 4 in 2020/2021. Only high infection levels reveal the effect of risk factors and socioeconomic inequalities. There may be confounding between the highly deprived areas and border regions in the north and east of Bavaria, making the relationship between area deprivation and infection burden more complex. Vaccination appeared to balance incidence and mortality rates between the most and least deprived districts. Vaccination makes an important contribution to health equality.
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Affiliation(s)
- Kirsi Marjaana Manz
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Ludwig-Maximilians-Universität (LMU), Munich, Germany
- *Correspondence: Kirsi Marjaana Manz
| | - Lars Schwettmann
- Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Institute of Health Economics and Health Care Management, Neuherberg, Germany
- Department of Economics, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Ulrich Mansmann
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Ludwig-Maximilians-Universität (LMU), Munich, Germany
- Pettenkofer School of Public Health, Ludwig-Maximilians-Universität (LMU), Munich, Germany
| | - Werner Maier
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Ludwig-Maximilians-Universität (LMU), Munich, Germany
- Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Institute of Health Economics and Health Care Management, Neuherberg, Germany
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Vranckx M, Faes C, Molenberghs G, Hens N, Beutels P, Van Damme P, Aerts J, Petrof O, Pepermans K, Neyens T. A spatial model to jointly analyze self-reported survey data of COVID-19 symptoms and official COVID-19 incidence data. Biom J 2022; 65:e2100186. [PMID: 35818698 PMCID: PMC9349774 DOI: 10.1002/bimj.202100186] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 05/06/2022] [Accepted: 05/21/2022] [Indexed: 01/17/2023]
Abstract
This work presents a joint spatial modeling framework to improve estimation of the spatial distribution of the latent COVID-19 incidence in Belgium, based on test-confirmed COVID-19 cases and crowd-sourced symptoms data as reported in a large-scale online survey. Correction is envisioned for stochastic dependence between the survey's response rate and spatial COVID-19 incidence, commonly known as preferential sampling, but not found significant. Results show that an online survey can provide valuable auxiliary data to optimize spatial COVID-19 incidence estimation based on confirmed cases in situations with limited testing capacity. Furthermore, it is shown that an online survey on COVID-19 symptoms with a sufficiently large sample size per spatial entity is capable of pinpointing the same locations that appear as test-confirmed clusters, approximately 1 week earlier. We conclude that a large-scale online study provides an inexpensive and flexible method to collect timely information of an epidemic during its early phase, which can be used by policy makers in an early phase of an epidemic and in conjunction with other monitoring systems.
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Affiliation(s)
- Maren Vranckx
- I‐BioStatData Science InstituteHasselt UniversityHasseltBelgium
| | - Christel Faes
- I‐BioStatData Science InstituteHasselt UniversityHasseltBelgium
| | - Geert Molenberghs
- I‐BioStatData Science InstituteHasselt UniversityHasseltBelgium,L‐BioStatDepartment of Public Health and Primary CareFaculty of MedicineKU LeuvenLeuvenBelgium
| | - Niel Hens
- I‐BioStatData Science InstituteHasselt UniversityHasseltBelgium,Center for Health Economics Research and Modeling Infectious DiseasesVaccine and Infectious Disease InstituteUniversity of AntwerpAntwerpBelgium
| | - Philippe Beutels
- Center for Health Economics Research and Modeling Infectious DiseasesVaccine and Infectious Disease InstituteUniversity of AntwerpAntwerpBelgium
| | - Pierre Van Damme
- Center for Health Economics Research and Modeling Infectious DiseasesVaccine and Infectious Disease InstituteUniversity of AntwerpAntwerpBelgium
| | - Jan Aerts
- I‐BioStatData Science InstituteHasselt UniversityHasseltBelgium
| | - Oana Petrof
- I‐BioStatData Science InstituteHasselt UniversityHasseltBelgium
| | - Koen Pepermans
- Faculty of Social SciencesUniversity of AntwerpAntwerpBelgium
| | - Thomas Neyens
- I‐BioStatData Science InstituteHasselt UniversityHasseltBelgium,L‐BioStatDepartment of Public Health and Primary CareFaculty of MedicineKU LeuvenLeuvenBelgium
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[Regional monitoring of infections by means of standardized case fatality rates using the example of SARS-CoV-2 in Bavaria]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2021; 64:1146-1156. [PMID: 34383083 PMCID: PMC8358915 DOI: 10.1007/s00103-021-03397-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 07/05/2021] [Indexed: 10/27/2022]
Abstract
BACKGROUND Maps of the temporal evolution of the regional distribution of a health-related measure enable public health-relevant assessments of health outcomes. OBJECTIVES The paper introduces the concept of standardized case fatality rate (sCFR). It describes the ratio of the regional variation in mortality to the regional variation in the documented infection process. The regional sCFR values are presented in maps and the time-varying regional heterogeneity observed in them is interpreted. MATERIALS AND METHODS The regional sCFR is the quotient of the regional standardized mortality and case rate. It is estimated using a bivariate model. The sCFR values presented in maps are based on SARS-CoV‑2 reporting data from Bavaria since the beginning of April 2020 until the end of March 2021. Four quarters (Q2/20, Q3/20, Q4/20, and Q1/21) are considered. RESULTS In the quarters considered, the naïve CFR values in Bavaria are 5.0%, 0.5%, 2.5%, and 2.8%. In Q2/20, regional sCFR values are irregularly distributed across the state. This heterogeneity weakens in the second wave of the epidemic. In Q1/21, only isolated regions with elevated sCFR (> 1.25) appear in southern Bavaria. Clusters of regions with sCFR > 1.25 form in northern Bavaria, with Oberallgäu being the region with the lowest sCFR (0.39, 95% credibility interval: 0.25-0.55). CONCLUSIONS In Bavaria, heterogeneous regional SARS-CoV-2-specific sCFR values are shown to change over time. They estimate the relative risk of dying from or with COVID-19 as a documented case. Strong small-scale variability in sCFR suggests a preference for regional over higher-level measures to manage the incidence of infection.
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Neyens T, Faes C, Vranckx M, Pepermans K, Hens N, Van Damme P, Molenberghs G, Aerts J, Beutels P. Can COVID-19 symptoms as reported in a large-scale online survey be used to optimise spatial predictions of COVID-19 incidence risk in Belgium? Spat Spatiotemporal Epidemiol 2020; 35:100379. [PMID: 33138946 PMCID: PMC7518805 DOI: 10.1016/j.sste.2020.100379] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 09/01/2020] [Accepted: 09/24/2020] [Indexed: 01/31/2023]
Abstract
Although COVID-19 has been spreading throughout Belgium since February, 2020, its spatial dynamics in Belgium remain poorly understood, partly due to the limited testing of suspected cases during the epidemic's early phase. We analyse data of COVID-19 symptoms, as self-reported in a weekly online survey, which is open to all Belgian citizens. We predict symptoms' incidence using binomial models for spatially discrete data, and we introduce these as a covariate in the spatial analysis of COVID-19 incidence, as reported by the Belgian government during the days following a survey round. The symptoms' incidence is moderately predictive of the variation in the relative risks based on the confirmed cases; exceedance probability maps of the symptoms' incidence and confirmed cases' relative risks overlap partly. We conclude that this framework can be used to detect COVID-19 clusters of substantial sizes, but it necessitates spatial information on finer scales to locate small clusters.
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Affiliation(s)
- Thomas Neyens
- I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, Hasselt B-3500, Belgium; I-BioStat, Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Kapucijnenvoer 35, Leuven B-3000, Belgium.
| | - Christel Faes
- I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, Hasselt B-3500, Belgium
| | - Maren Vranckx
- I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, Hasselt B-3500, Belgium
| | - Koen Pepermans
- Faculty of Social Sciences, University of Antwerp, Sint-Jacobstraat 2, Antwerp 2000, Belgium
| | - Niel Hens
- I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, Hasselt B-3500, Belgium; Center for Health Economics Research and Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Antwerp 2610, Belgium
| | - Pierre Van Damme
- Center for Health Economics Research and Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Antwerp 2610, Belgium
| | - Geert Molenberghs
- I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, Hasselt B-3500, Belgium; I-BioStat, Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Kapucijnenvoer 35, Leuven B-3000, Belgium
| | - Jan Aerts
- I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, Hasselt B-3500, Belgium
| | - Philippe Beutels
- Center for Health Economics Research and Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Antwerp 2610, Belgium
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Neyens T, Lawson AB, Kirby RS, Nuyts V, Watjou K, Aregay M, Carroll R, Nawrot TS, Faes C. Disease mapping of zero-excessive mesothelioma data in Flanders. Ann Epidemiol 2016; 27:59-66.e3. [PMID: 27908590 DOI: 10.1016/j.annepidem.2016.10.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2016] [Revised: 10/04/2016] [Accepted: 10/04/2016] [Indexed: 11/18/2022]
Abstract
PURPOSE To investigate the distribution of mesothelioma in Flanders using Bayesian disease mapping models that account for both an excess of zeros and overdispersion. METHODS The numbers of newly diagnosed mesothelioma cases within all Flemish municipalities between 1999 and 2008 were obtained from the Belgian Cancer Registry. To deal with overdispersion, zero inflation, and geographical association, the hurdle combined model was proposed, which has three components: a Bernoulli zero-inflation mixture component to account for excess zeros, a gamma random effect to adjust for overdispersion, and a normal conditional autoregressive random effect to attribute spatial association. This model was compared with other existing methods in literature. RESULTS The results indicate that hurdle models with a random effects term accounting for extra variance in the Bernoulli zero-inflation component fit the data better than hurdle models that do not take overdispersion in the occurrence of zeros into account. Furthermore, traditional models that do not take into account excessive zeros but contain at least one random effects term that models extra variance in the counts have better fits compared to their hurdle counterparts. In other words, the extra variability, due to an excess of zeros, can be accommodated by spatially structured and/or unstructured random effects in a Poisson model such that the hurdle mixture model is not necessary. CONCLUSIONS Models taking into account zero inflation do not always provide better fits to data with excessive zeros than less complex models. In this study, a simple conditional autoregressive model identified a cluster in mesothelioma cases near a former asbestos processing plant (Kapelle-op-den-Bos). This observation is likely linked with historical local asbestos exposures. Future research will clarify this.
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Affiliation(s)
- Thomas Neyens
- Department of Sciences, I-BioStat, University of Hasselt, Hasselt, Belgium.
| | - Andrew B Lawson
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Medical University of South Carolina, Charleston
| | - Russell S Kirby
- Department of Community and Family Health, College of Public Health, University of South Florida, Tampa
| | - Valerie Nuyts
- Department of Public Health and Primary Care, Centre for Environment and Health, KU Leuven, Leuven, Belgium
| | - Kevin Watjou
- Department of Sciences, I-BioStat, University of Hasselt, Hasselt, Belgium
| | - Mehreteab Aregay
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Medical University of South Carolina, Charleston
| | - Rachel Carroll
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Medical University of South Carolina, Charleston
| | - Tim S Nawrot
- Department of Public Health and Primary Care, Centre for Environment and Health, KU Leuven, Leuven, Belgium; Department of Sciences, Centre for Environmental Sciences, University of Hasselt, Hasselt, Belgium
| | - Christel Faes
- Department of Sciences, I-BioStat, University of Hasselt, Hasselt, Belgium
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