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Cruz RFD, Ruberti JA, Mota TS, Silveira LVDA, Chiaravalloti-Neto F. Spatiotemporal Bayesian modeling of the risk of congenital syphilis in São Paulo, SP, Brazil. Spat Spatiotemporal Epidemiol 2024; 49:100651. [PMID: 38876564 DOI: 10.1016/j.sste.2024.100651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 04/03/2024] [Accepted: 04/10/2024] [Indexed: 06/16/2024]
Abstract
The aim of this study is to analyze the spatiotemporal risk of congenital syphilis (CS) in high-prevalence areas in the city of São Paulo, SP, Brazil, and to evaluate its relationship with socioeconomic, demographic, and environmental variables. An ecological study was conducted based on secondary CS data with spatiotemporal components collected from 310 areas between 2010 and 2016. The data were modeled in a Bayesian context using the integrated nested Laplace approximation (INLA) method. Risk maps showed an increasing CS trend over time and highlighted the areas that presented the highest and lowest risk in each year. The model showed that the factors positively associated with a higher risk of CS were the Gini index and the proportion of women aged 18-24 years without education or with incomplete primary education, while the factors negatively associated were the proportion of women of childbearing age and the mean per capita income.
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Affiliation(s)
- Renato Ferreira da Cruz
- Institute of Exact and Earth Sciences, Araguaia University Campus - Unit II, Federal University of Mato Grosso, 6390 Valdon Varjão Avenue, Barra do Garca̧s, Mato Grosso, 78605-091, Brazil.
| | | | | | - Liciana Vaz de Arruda Silveira
- Institute of Biosciences, Department of Biostatistics, São Paulo State University Júlio de Mesquita Filho, Botucatu, São Paulo, Brazil.
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Areed WD, Price A, Thompson H, Malseed R, Mengersen K. Spatial non-parametric Bayesian clustered coefficients. Sci Rep 2024; 14:9677. [PMID: 38678077 PMCID: PMC11055928 DOI: 10.1038/s41598-024-59973-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 04/17/2024] [Indexed: 04/29/2024] Open
Abstract
In the field of population health research, understanding the similarities between geographical areas and quantifying their shared effects on health outcomes is crucial. In this paper, we synthesise a number of existing methods to create a new approach that specifically addresses this goal. The approach is called a Bayesian spatial Dirichlet process clustered heterogeneous regression model. This non-parametric framework allows for inference on the number of clusters and the clustering configurations, while simultaneously estimating the parameters for each cluster. We demonstrate the efficacy of the proposed algorithm using simulated data and further apply it to analyse influential factors affecting children's health development domains in Queensland. The study provides valuable insights into the contributions of regional similarities in education and demographics to health outcomes, aiding targeted interventions and policy design.
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Affiliation(s)
- Wala Draidi Areed
- School of Mathematical Science, Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia.
| | - Aiden Price
- School of Mathematical Science, Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia
| | - Helen Thompson
- School of Mathematical Science, Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia
| | - Reid Malseed
- Children's Health Queensland, Brisbane, QLD, Australia
| | - Kerrie Mengersen
- School of Mathematical Science, Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia
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3
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Balocchi C, Deshpande SK, George EI, Jensen ST. Crime in Philadelphia: Bayesian Clustering with Particle Optimization. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2156348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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4
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MacNab YC. Bayesian disease mapping: Past, present, and future. SPATIAL STATISTICS 2022; 50:100593. [PMID: 35075407 PMCID: PMC8769562 DOI: 10.1016/j.spasta.2022.100593] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 01/06/2022] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
On the occasion of the Spatial Statistics' 10th Anniversary, I reflect on the past and present of Bayesian disease mapping and look into its future. I focus on some key developments of models, and on recent evolution of multivariate and adaptive Gaussian Markov random fields and their impact and importance in disease mapping. I reflect on Bayesian disease mapping as a subject of spatial statistics that has advanced to date, and continues to grow, in scope and complexity alongside increasing needs of analytic tools for contemporary health science research, such as spatial epidemiology, population and public health, and medicine. I illustrate (potential) utility and impact of some of the disease mapping models and methods for analysing and monitoring communicable disease such as the COVID-19 infection risks during an ongoing pandemic.
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Affiliation(s)
- Ying C MacNab
- School of Population and Public Health, University of British Columbia, Vancouver, Canada
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5
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Abstract
Spatial documentation is exponentially increasing given the availability of Big Data in the Internet of Things, enabled by device miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns in space through prior knowledge and data likelihood. However, this class of modeling is not yet well explored when compared to adopting classification and regression in machine-learning models, in which the assumption of the spatiotemporal independence of the data is often made, that is an inexistent or very weak dependence. Thus, this systematic review aims to address the main models presented in the literature over the past 20 years, identifying the gaps and research opportunities. Elements such as random fields, spatial domains, prior specification, the covariance function, and numerical approximations are discussed. This work explores the two subclasses of spatial smoothing: global and local.
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6
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Warren JL, Cai J, Johnson NP, Deziel NC. A discrete kernel stick‐breaking model for detecting spatial boundaries in hydraulic fracturing wastewater disposal well placement across Ohio. J R Stat Soc Ser C Appl Stat 2021. [DOI: 10.1111/rssc.12527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Joshua L. Warren
- Department of Biostatistics Yale University New Haven Connecticut USA
| | - Jiachen Cai
- Department of Biostatistics Yale University New Haven Connecticut USA
| | - Nicholaus P. Johnson
- Department of Environmental Health Sciences Yale University New Haven Connecticut USA
| | - Nicole C. Deziel
- Department of Environmental Health Sciences Yale University New Haven Connecticut USA
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7
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Radon K, Bakuli A, Pütz P, Le Gleut R, Guggenbuehl Noller JM, Olbrich L, Saathoff E, Garí M, Schälte Y, Frahnow T, Wölfel R, Pritsch M, Rothe C, Pletschette M, Rubio-Acero R, Beyerl J, Metaxa D, Forster F, Thiel V, Castelletti N, Rieß F, Diefenbach MN, Fröschl G, Bruger J, Winter S, Frese J, Puchinger K, Brand I, Kroidl I, Wieser A, Hoelscher M, Hasenauer J, Fuchs C. From first to second wave: follow-up of the prospective COVID-19 cohort (KoCo19) in Munich (Germany). BMC Infect Dis 2021; 21:925. [PMID: 34493217 PMCID: PMC8423599 DOI: 10.1186/s12879-021-06589-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 08/19/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND In the 2nd year of the COVID-19 pandemic, knowledge about the dynamics of the infection in the general population is still limited. Such information is essential for health planners, as many of those infected show no or only mild symptoms and thus, escape the surveillance system. We therefore aimed to describe the course of the pandemic in the Munich general population living in private households from April 2020 to January 2021. METHODS The KoCo19 baseline study took place from April to June 2020 including 5313 participants (age 14 years and above). From November 2020 to January 2021, we could again measure SARS-CoV-2 antibody status in 4433 of the baseline participants (response 83%). Participants were offered a self-sampling kit to take a capillary blood sample (dry blood spot; DBS). Blood was analysed using the Elecsys® Anti-SARS-CoV-2 assay (Roche). Questionnaire information on socio-demographics and potential risk factors assessed at baseline was available for all participants. In addition, follow-up information on health-risk taking behaviour and number of personal contacts outside the household (N = 2768) as well as leisure time activities (N = 1263) were collected in summer 2020. RESULTS Weighted and adjusted (for specificity and sensitivity) SARS-CoV-2 sero-prevalence at follow-up was 3.6% (95% CI 2.9-4.3%) as compared to 1.8% (95% CI 1.3-3.4%) at baseline. 91% of those tested positive at baseline were also antibody-positive at follow-up. While sero-prevalence increased from early November 2020 to January 2021, no indication of geospatial clustering across the city of Munich was found, although cases clustered within households. Taking baseline result and time to follow-up into account, men and participants in the age group 20-34 years were at the highest risk of sero-positivity. In the sensitivity analyses, differences in health-risk taking behaviour, number of personal contacts and leisure time activities partly explained these differences. CONCLUSION The number of citizens in Munich with SARS-CoV-2 antibodies was still below 5% during the 2nd wave of the pandemic. Antibodies remained present in the majority of SARS-CoV-2 sero-positive baseline participants. Besides age and sex, potentially confounded by differences in behaviour, no major risk factors could be identified. Non-pharmaceutical public health measures are thus still important.
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Affiliation(s)
- Katja Radon
- Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, 80336, Munich, Germany.
- Center for International Health (CIH), University Hospital, LMU Munich, 80336, Munich, Germany.
- Comprehensive Pneumology Center (CPC) Munich, German Center for Lung Research (DZL), 89337, Munich, Germany.
| | - Abhishek Bakuli
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Peter Pütz
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Faculty of Business Administration and Economics, Bielefeld University, 33615, Bielefeld, Germany
| | - Ronan Le Gleut
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Core Facility Statistical Consulting, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | | | - Laura Olbrich
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
- German Center for Infection Research (DZIF), partner site, Munich, Germany
| | - Elmar Saathoff
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
- German Center for Infection Research (DZIF), partner site, Munich, Germany
| | - Mercè Garí
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Yannik Schälte
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, 85748, Garching, Germany
| | - Turid Frahnow
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Faculty of Business Administration and Economics, Bielefeld University, 33615, Bielefeld, Germany
| | - Roman Wölfel
- German Center for Infection Research (DZIF), partner site, Munich, Germany
- Bundeswehr Institute of Microbiology, 80937, Munich, Germany
| | - Michael Pritsch
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
- German Center for Infection Research (DZIF), partner site, Munich, Germany
| | - Camilla Rothe
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Michel Pletschette
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Raquel Rubio-Acero
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Jessica Beyerl
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Dafni Metaxa
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Felix Forster
- Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, 80336, Munich, Germany
- Comprehensive Pneumology Center (CPC) Munich, German Center for Lung Research (DZL), 89337, Munich, Germany
| | - Verena Thiel
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Noemi Castelletti
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Friedrich Rieß
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
- German Center for Infection Research (DZIF), partner site, Munich, Germany
| | - Maximilian N Diefenbach
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Günter Fröschl
- Center for International Health (CIH), University Hospital, LMU Munich, 80336, Munich, Germany
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Jan Bruger
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Simon Winter
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Jonathan Frese
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Kerstin Puchinger
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Isabel Brand
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
| | - Inge Kroidl
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
- German Center for Infection Research (DZIF), partner site, Munich, Germany
| | - Andreas Wieser
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
- German Center for Infection Research (DZIF), partner site, Munich, Germany
| | - Michael Hoelscher
- Center for International Health (CIH), University Hospital, LMU Munich, 80336, Munich, Germany
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, 80802, Munich, Germany
- German Center for Infection Research (DZIF), partner site, Munich, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, 85748, Garching, Germany
- Interdisciplinary Research Unit Mathematics and Life Sciences, University of Bonn, 53113, Bonn, Germany
| | - Christiane Fuchs
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Faculty of Business Administration and Economics, Bielefeld University, 33615, Bielefeld, Germany
- Core Facility Statistical Consulting, Helmholtz Zentrum München-German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, 85748, Garching, Germany
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8
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Miranda ML, Callender R, Canales JM, Craft E, Ensor KB, Grossman M, Hopkins L, Johnston J, Shah U, Tootoo J. The Texas flood registry: a flexible tool for environmental and public health practitioners and researchers. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2021; 31:823-831. [PMID: 34175888 PMCID: PMC8234769 DOI: 10.1038/s41370-021-00347-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 05/24/2021] [Accepted: 05/25/2021] [Indexed: 05/19/2023]
Abstract
BACKGROUND Making landfall in Rockport, Texas in August 2017, Hurricane Harvey resulted in unprecedented flooding, displacing tens of thousands of people, and creating environmental hazards and exposures for many more. OBJECTIVE We describe a collaborative project to establish the Texas Flood Registry to track the health and housing impacts of major flooding events. METHODS Those who enroll in the registry answer retrospective questions regarding the impact of storms on their health and housing status. We recruit both those who did and did not flood during storm events to enable key comparisons. We leverage partnerships with multiple local health departments, community groups, and media outlets to recruit broadly. We performed a preliminary analysis using multivariable logistic regression and a binomial Bayesian conditional autoregressive (CAR) spatial model. RESULTS We find that those whose homes flooded, or who came into direct skin contact with flood water, are more likely to experience a series of self-reported health effects. Median household income is inversely related to adverse health effects, and spatial analysis provides important insights within the modeling approach. SIGNIFICANCE Global climate change is likely to increase the number and intensity of rainfall events, resulting in additional health burdens. Population-level data on the health and housing impacts of major flooding events is imperative in preparing for our planet's future.
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Affiliation(s)
- Marie Lynn Miranda
- Children's Environmental Health Initiative, University of Notre Dame, South Bend, IN, USA.
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, South Bend, IN, USA.
| | - Rashida Callender
- Children's Environmental Health Initiative, Rice University, Houston, TX, USA
| | - Joally M Canales
- Children's Environmental Health Initiative, Rice University, Houston, TX, USA
| | | | | | - Max Grossman
- Children's Environmental Health Initiative, University of Notre Dame, South Bend, IN, USA
| | - Loren Hopkins
- Department of Statistics, Rice University, Houston, TX, USA
- Houston Health Department, Houston, TX, USA
| | - Jocelyn Johnston
- Children's Environmental Health Initiative, University of Notre Dame, South Bend, IN, USA
| | - Umair Shah
- Harris County Public Health, Houston, TX, USA
- Washington State Department of Health, Olympia, WA, USA
| | - Joshua Tootoo
- Children's Environmental Health Initiative, University of Notre Dame, South Bend, IN, USA
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Zhelyazkova M, Yordanova R, Mihaylov I, Kirov S, Tsonev S, Danko D, Mason C, Vassilev D. Origin Sample Prediction and Spatial Modeling of Antimicrobial Resistance in Metagenomic Sequencing Data. Front Genet 2021; 12:642991. [PMID: 33763122 PMCID: PMC7983949 DOI: 10.3389/fgene.2021.642991] [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: 12/17/2020] [Accepted: 02/02/2021] [Indexed: 12/18/2022] Open
Abstract
The steady elaboration of the Metagenomic and Metadesign of Subways and Urban Biomes (MetaSUB) international consortium project raises important new questions about the origin, variation, and antimicrobial resistance of the collected samples. CAMDA (Critical Assessment of Massive Data Analysis, http://camda.info/) forum organizes annual challenges where different bioinformatics and statistical approaches are tested on samples collected around the world for bacterial classification and prediction of geographical origin. This work proposes a method which not only predicts the locations of unknown samples, but also estimates the relative risk of antimicrobial resistance through spatial modeling. We introduce a new component in the standard analysis as we apply a Bayesian spatial convolution model which accounts for spatial structure of the data as defined by the longitude and latitude of the samples and assess the relative risk of antimicrobial resistance taxa across regions which is relevant to public health. We can then use the estimated relative risk as a new measure for antimicrobial resistance. We also compare the performance of several machine learning methods, such as Gradient Boosting Machine, Random Forest, and Neural Network to predict the geographical origin of the mystery samples. All three methods show consistent results with some superiority of Random Forest classifier. In our future work we can consider a broader class of spatial models and incorporate covariates related to the environment and climate profiles of the samples to achieve more reliable estimation of the relative risk related to antimicrobial resistance.
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Affiliation(s)
- Maya Zhelyazkova
- Faculty of Mathematics and Informatics, Sofia University St. Kliment Ohridski, Sofia, Bulgaria
| | - Roumyana Yordanova
- Department of Mathematics, Hokkaido University, Sapporo, Japan.,Bulgarian Academy of Sciences, Institute of Mathematics and Informatics, Sofia, Bulgaria
| | - Iliyan Mihaylov
- Faculty of Mathematics and Informatics, Sofia University St. Kliment Ohridski, Sofia, Bulgaria
| | - Stefan Kirov
- Bristol-Myers Squibb, Pennington, NJ, United States
| | - Stefan Tsonev
- Department of Molecular Genetics, AgroBioInstitute, Sofia, Bulgaria
| | - David Danko
- Department of Computational Informatics, Weill Cornell Medical College, New York, NY, United States
| | | | - Dimitar Vassilev
- Faculty of Mathematics and Informatics, Sofia University St. Kliment Ohridski, Sofia, Bulgaria
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10
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Pietrosanu M, Jiang B. Discussion of “Statistical disease mapping for heterogeneous neuroimaging studies”. CAN J STAT 2021. [DOI: 10.1002/cjs.11602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Matthew Pietrosanu
- Department of Mathematical and Statistical Sciences University of Alberta Edmonton T6G 2G1 Alberta Canada
| | - Bei Jiang
- Department of Mathematical and Statistical Sciences University of Alberta Edmonton T6G 2G1 Alberta Canada
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11
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Aswi A, Cramb S, Duncan E, Mengersen K. Evaluating the impact of a small number of areas on spatial estimation. Int J Health Geogr 2020; 19:39. [PMID: 32977803 PMCID: PMC7519538 DOI: 10.1186/s12942-020-00233-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 09/15/2020] [Indexed: 11/10/2022] Open
Abstract
Background There is an expanding literature on different representations of spatial random effects for different types of spatial correlation structure within the conditional autoregressive class of priors for Bayesian spatial models. However, little is known about the impact of these different priors when the number of areas is small. This paper aimed to investigate this problem both in the context of a case study of spatial analysis of dengue fever and more generally through a simulation study. Methods Both the simulation study and the case study considered count data aggregated to a small area level in a region. Five different conditional autoregressive priors for a simple Bayesian Poisson model were considered: independent, Besag-York-Mollié, Leroux, and two variants of a localised clustering model. Data were simulated with eight different sizes of areal grids, ranging from 4 to 2500 areas, and two different levels of both spatial autocorrelation and disease counts. Model goodness-of-fit measures and model estimates were compared. A case study involving dengue fever cases in 14 local areas in Makassar, Indonesia, was also considered. Results The simulation study showed that model performance varied under different scenarios. When areas had low autocorrelation and high counts, and the number of areas was at most 25, the BYM, Leroux and localised \documentclass[12pt]{minimal}
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\begin{document}$$G = 2$$\end{document}G=2 models performed similarly and better than the independent and localised \documentclass[12pt]{minimal}
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\begin{document}$$G = 3$$\end{document}G=3 models. However, when the number of areas were at least 100, all models performed differently, and the Leroux model performed the best. Overall, the Leroux model performed the best for every scenario especially when there were at least 16 areas. Based on the case study, the comparative performance of spatial models may also vary for a small number of areas, especially when the data have a relatively large mean and variance over areas. In this case, the localised model with G = 3 was a better choice. Conclusion Detecting spatial patterns can be difficult when there are very few areas. Understanding the characteristics of the data and the relative influence of alternative conditional autoregressive priors is essential in selecting an appropriate Bayesian spatial model.
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Affiliation(s)
- Aswi Aswi
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Australia
| | - Susanna Cramb
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Australia. .,School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia.
| | - Earl Duncan
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Australia
| | - Kerrie Mengersen
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Australia
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12
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Cespedes MI, McGree JM, Drovandi CC, Mengersen K, Fripp J, Doecke JD. Relative rate of change in cognitive score network dynamics via Bayesian hierarchical models reveal spatial patterns of neurodegeneration. Stat Med 2020; 39:2695-2713. [PMID: 32419227 DOI: 10.1002/sim.8568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Revised: 04/15/2020] [Accepted: 04/16/2020] [Indexed: 11/11/2022]
Abstract
The degeneration of the human brain is a complex process, which often affects certain brain regions due to healthy aging or disease. This degeneration can be evaluated on regions of interest (ROI) in the brain through probabilistic networks and morphological estimates. Current approaches for finding such networks are limited to analyses at discrete neuropsychological stages, which cannot appropriately account for connectivity dynamics over the onset of cognitive deterioration, and morphological changes are seldom unified with connectivity networks, despite known dependencies. To overcome these limitations, a probabilistic wombling model is proposed to simultaneously estimate ROI cortical thickness and covariance networks contingent on rates of change in cognitive decline. This proposed model was applied to analyze longitudinal data from healthy control (HC) and Alzheimer's disease (AD) groups and found connection differences pertaining to regions, which play a crucial role in lasting cognitive impairment, such as the entorhinal area and temporal regions. Moreover, HC cortical thickness estimates were significantly higher than those in the AD group across all ROIs. The analyses presented in this work will help practitioners jointly analyze brain tissue atrophy at the ROI-level conditional on neuropsychological networks, which could potentially allow for more targeted therapeutic interventions.
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Affiliation(s)
- Marcela I Cespedes
- CSIRO Health and Biosecurity, Australian E-Health Research Centre, Herston, Queensland, Australia
| | - James M McGree
- ARC Centre for Mathematical and Statistical Frontiers and School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Christopher C Drovandi
- ARC Centre for Mathematical and Statistical Frontiers and School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Kerrie Mengersen
- ARC Centre for Mathematical and Statistical Frontiers and School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian E-Health Research Centre, Herston, Queensland, Australia
| | - James D Doecke
- CSIRO Health and Biosecurity, Australian E-Health Research Centre, Herston, Queensland, Australia
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13
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Aswi A, Cramb S, Duncan E, Hu W, White G, Mengersen K. Climate variability and dengue fever in Makassar, Indonesia: Bayesian spatio-temporal modelling. Spat Spatiotemporal Epidemiol 2020; 33:100335. [PMID: 32370940 DOI: 10.1016/j.sste.2020.100335] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 11/10/2019] [Accepted: 12/04/2019] [Indexed: 12/01/2022]
Abstract
A range of Bayesian models have been used to describe spatial and temporal patterns of disease in areal unit data. In this study, we applied two Bayesian spatio-temporal conditional autoregressive (ST CAR) models, one of which allows discontinuities in risk between neighbouring areas (creating 'groups'), to examine dengue fever patterns. Data on annual (2002-2017) and monthly (January 2013 - December 2017) dengue cases and climatic factors over 14 geographic areas were obtained for Makassar, Indonesia. Combinations of covariates and model formulations were compared considering credible intervals, overall goodness of fit, and the grouping structure. For annual data, an ST CAR localised model incorporating average humidity provided the best fit, while for monthly data, a single-group ST CAR autoregressive model incorporating rainfall and average humidity was preferred. Using appropriate Bayesian spatio-temporal models enables identification of different groups of areas and the impact of climatic covariates which may help inform policy decisions.
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Affiliation(s)
- Aswi Aswi
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Australia; Universitas Negeri Makassar, Indonesia; Science and Engineering Faculty, School of Mathematical Science, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland 4001, Australia
| | - Susanna Cramb
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Australia; School of Public Health and Social Work, Queensland University of Technology, Australia
| | - Earl Duncan
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Australia; Science and Engineering Faculty, School of Mathematical Science, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland 4001, Australia
| | - Wenbiao Hu
- School of Public Health and Social Work, Queensland University of Technology, Australia
| | - Gentry White
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Australia; Science and Engineering Faculty, School of Mathematical Science, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland 4001, Australia
| | - Kerrie Mengersen
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Australia; Science and Engineering Faculty, School of Mathematical Science, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland 4001, Australia.
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14
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Balocchi C, Jensen ST. Spatial modeling of trends in crime over time in Philadelphia. Ann Appl Stat 2019. [DOI: 10.1214/19-aoas1280] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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15
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Santos N, Nunes T, Fonseca C, Vieira-Pinto M, Almeida V, Gortázar C, Correia-Neves M. Spatial Analysis of Wildlife Tuberculosis Based on a Serologic Survey Using Dried Blood Spots, Portugal. Emerg Infect Dis 2019; 24:2169-2175. [PMID: 30457522 PMCID: PMC6256377 DOI: 10.3201/eid2412.171357] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
We investigated the spatial epidemiology of bovine tuberculosis (TB) in wildlife in a multihost system. We surveyed bovine TB in Portugal by serologic analysis of elutes of dried blood spots obtained from hunted wild boar. We modeled spatial disease risk by using areal generalized linear mixed models with conditional autoregressive priors. Antibodies against Mycobaterium bovis were detected in 2.4% (95% CI 1.5%-3.8%) of 678 wild boar in 2 geographic clusters, and the predicted risk fits well with independent reports of M. bovis culture. Results show that elutes are an almost perfect substitute for serum (Cohen unweighted κ = 0.818), indicating that serologic tests coupled with dried blood spots are an effective strategy for large-scale bovine TB surveys, using wild boar as sentinel species. Results also show that bovine TB is an emerging wildlife disease and stress the need to prevent further geographic spread and prevalence increase.
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Bakar KS, Jin H. Areal prediction of survey data using Bayesian spatial generalised linear models. COMMUN STAT-SIMUL C 2018. [DOI: 10.1080/03610918.2018.1530787] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- K. Shuvo Bakar
- Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, Australia
- ANU Centre for Social Research and Methods, The Australian National University, Canberra, Australia
| | - Huidong Jin
- Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, Australia
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Gonsalves GS, Copple JT, Johnson T, Paltiel AD, Warren JL. Bayesian adaptive algorithms for locating HIV mobile testing services. BMC Med 2018; 16:155. [PMID: 30173667 PMCID: PMC6120098 DOI: 10.1186/s12916-018-1129-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 07/13/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND We have previously conducted computer-based tournaments to compare the yield of alternative approaches to deploying mobile HIV testing services in settings where the prevalence of undetected infection may be characterized by 'hotspots'. We report here on three refinements to our prior assessments and their implications for decision-making. Specifically, (1) enlarging the number of geographic zones; (2) including spatial correlation in the prevalence of undetected infection; and (3) evaluating a prospective search algorithm that accounts for such correlation. METHODS Building on our prior work, we used a simulation model to create a hypothetical city consisting of up to 100 contiguous geographic zones. Each zone was randomly assigned a prevalence of undetected HIV infection. We employed a user-defined weighting scheme to correlate infection levels between adjacent zones. Over 180 days, search algorithms selected a zone in which to conduct a fixed number of HIV tests. Algorithms were permitted to observe the results of their own prior testing activities and to use that information in choosing where to test in subsequent rounds. The algorithms were (1) Thompson sampling (TS), an adaptive Bayesian search strategy; (2) Besag York Mollié (BYM), a Bayesian hierarchical model; and (3) Clairvoyance, a benchmarking strategy with access to perfect information. RESULTS Over 250 tournament runs, BYM detected 65.3% (compared to 55.1% for TS) of the cases identified by Clairvoyance. BYM outperformed TS in all sensitivity analyses, except when there was a small number of zones (i.e., 16 zones in a 4 × 4 grid), wherein there was no significant difference in the yield of the two strategies. Though settings of no, low, medium, and high spatial correlation in the data were examined, differences in these levels did not have a significant effect on the relative performance of BYM versus TS. CONCLUSIONS BYM narrowly outperformed TS in our simulation, suggesting that small improvements in yield can be achieved by accounting for spatial correlation. However, the comparative simplicity with which TS can be implemented makes a field evaluation critical to understanding the practical value of either of these algorithms as an alternative to existing approaches for deploying HIV testing resources.
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Affiliation(s)
- Gregg S. Gonsalves
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, 60 College Street, New Haven, CT USA
| | - J. Tyler Copple
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, 60 College Street, New Haven, CT USA
- Independent Consultant, Yale School of Public Health, 60 College Street, New Haven, CT USA
| | - Tyler Johnson
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, 60 College Street, New Haven, CT USA
| | - A. David Paltiel
- Department of Health Policy and Management, Yale School of Public Health, 60 College Street, New Haven, CT USA
| | - Joshua L. Warren
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT USA
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Kandhasamy C, Ghosh K. Relative risk for HIV in India - An estimate using conditional auto-regressive models with Bayesian approach. Spat Spatiotemporal Epidemiol 2017; 20:27-34. [PMID: 28137675 DOI: 10.1016/j.sste.2017.01.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2014] [Revised: 09/14/2016] [Accepted: 01/01/2017] [Indexed: 11/28/2022]
Abstract
Indian states are currently classified into HIV-risk categories based on the observed prevalence counts, percentage of infected attendees in antenatal clinics, and percentage of infected high-risk individuals. This method, however, does not account for the spatial dependence among the states nor does it provide any measure of statistical uncertainty. We provide an alternative model-based approach to address these issues. Our method uses Poisson log-normal models having various conditional autoregressive structures with neighborhood-based and distance-based weight matrices and incorporates all available covariate information. We use R and WinBugs software to fit these models to the 2011 HIV data. Based on the Deviance Information Criterion, the convolution model using distance-based weight matrix and covariate information on female sex workers, literacy rate and intravenous drug users is found to have the best fit. The relative risk of HIV for the various states is estimated using the best model and the states are then classified into the risk categories based on these estimated values. An HIV risk map of India is constructed based on these results. The choice of the final model suggests that an HIV control strategy which focuses on the female sex workers, intravenous drug users and literacy rate would be most effective.
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Affiliation(s)
| | - Kaushik Ghosh
- Department of Mathematical Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA
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19
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Small area-level variation in the incidence of psychotic disorders in an urban area in France: an ecological study. Soc Psychiatry Psychiatr Epidemiol 2016; 51:951-60. [PMID: 27189208 DOI: 10.1007/s00127-016-1231-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Accepted: 04/26/2016] [Indexed: 10/21/2022]
Abstract
PURPOSE We sought to determine whether significant variation in the incidence of clinically relevant psychoses existed at an ecological level in an urban French setting, and to examine possible factors associated with this variation. We aimed to advance the literature by testing this hypothesis in a novel population setting and by comparing a variety of spatial models. METHODS We sought to identify all first episode cases of non-affective and affective psychotic disorders presenting in a defined urban catchment area over a 4 years period, over more than half a million person-years at-risk. Because data from geographic close neighbourhoods usually show spatial autocorrelation, we used for our analyses Bayesian modelling. We included small area neighbourhood measures of deprivation, migrants' density and social fragmentation as putative explanatory variables in the models. RESULTS Incidence of broad psychotic disorders shows spatial patterning with the best fit for models that included both strong autocorrelation between neighbouring areas and weak autocorrelation between areas further apart. Affective psychotic disorders showed similar spatial patterning and were associated with the proportion of migrants/foreigners in the area (inverse correlation). In contrast, non-affective psychoses did not show spatial patterning. CONCLUSIONS At ecological level, the variation in the number of cases and the factors that influence this variation are different for non-affective and affective psychotic disorders. Important differences in results-compared with previous studies in different settings-point to the importance of the context and the necessity of further studies to understand these differences.
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21
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Law J. Exploring the Specifications of Spatial Adjacencies and Weights in Bayesian Spatial Modeling with Intrinsic Conditional Autoregressive Priors in a Small-area Study of Fall Injuries. AIMS Public Health 2016; 3:65-82. [PMID: 29546147 PMCID: PMC5690264 DOI: 10.3934/publichealth.2016.1.65] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Accepted: 03/02/2016] [Indexed: 11/18/2022] Open
Abstract
Intrinsic conditional autoregressive modeling in a Bayeisan hierarchical framework has been increasingly applied in small-area ecological studies. This study explores the specifications of spatial structure in this Bayesian framework in two aspects: adjacency, i.e., the set of neighbor(s) for each area; and (spatial) weight for each pair of neighbors. Our analysis was based on a small-area study of falling injuries among people age 65 and older in Ontario, Canada, that was aimed to estimate risks and identify risk factors of such falls. In the case study, we observed incorrect adjacencies information caused by deficiencies in the digital map itself. Further, when equal weights was replaced by weights based on a variable of expected count, the range of estimated risks increased, the number of areas with probability of estimated risk greater than one at different probability thresholds increased, and model fit improved. More importantly, significance of a risk factor diminished. Further research to thoroughly investigate different methods of variable weights; quantify the influence of specifications of spatial weights; and develop strategies for better defining spatial structure of a map in small-area analysis in Bayesian hierarchical spatial modeling is recommended.
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Affiliation(s)
- Jane Law
- School of Public Health and Health Systems, University of Waterloo, ON, Canada
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22
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Anderson C, Lee D, Dean N. Bayesian cluster detection via adjacency modelling. Spat Spatiotemporal Epidemiol 2016; 16:11-20. [DOI: 10.1016/j.sste.2015.11.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Revised: 08/13/2015] [Accepted: 11/12/2015] [Indexed: 11/25/2022]
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Rushworth A, Lee D, Mitchell R. A spatio-temporal model for estimating the long-term effects of air pollution on respiratory hospital admissions in Greater London. Spat Spatiotemporal Epidemiol 2014; 10:29-38. [PMID: 25113589 DOI: 10.1016/j.sste.2014.05.001] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2013] [Revised: 02/05/2014] [Accepted: 05/06/2014] [Indexed: 11/30/2022]
Abstract
It has long been known that air pollution is harmful to human health, as many epidemiological studies have been conducted into its effects. Collectively, these studies have investigated both the acute and chronic effects of pollution, with the latter typically based on individual level cohort designs that can be expensive to implement. As a result of the increasing availability of small-area statistics, ecological spatio-temporal study designs are also being used, with which a key statistical problem is allowing for residual spatio-temporal autocorrelation that remains after the covariate effects have been removed. We present a new model for estimating the effects of air pollution on human health, which allows for residual spatio-temporal autocorrelation, and a study into the long-term effects of air pollution on human health in Greater London, England. The individual and joint effects of different pollutants are explored, via the use of single pollutant models and multiple pollutant indices.
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Affiliation(s)
- Alastair Rushworth
- School of Mathematics and Statistics, University Gardens, University of Glasgow, Glasgow G12 8QW, UK.
| | - Duncan Lee
- School of Mathematics and Statistics, University Gardens, University of Glasgow, Glasgow G12 8QW, UK
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Mitchell R, Lee D. Is There Really a “Wrong Side of the Tracks”in Urban Areas and Does It Matter for Spatial Analysis? ACTA ACUST UNITED AC 2014. [DOI: 10.1080/00045608.2014.892321] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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25
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Affiliation(s)
- Craig Anderson
- School of Mathematics and Statistics, University of Glasgow, 15 University Gardens, Glasgow G12 8QQ, UK
| | - Duncan Lee
- School of Mathematics and Statistics, University of Glasgow, 15 University Gardens, Glasgow G12 8QQ, UK
| | - Nema Dean
- School of Mathematics and Statistics, University of Glasgow, 15 University Gardens, Glasgow G12 8QQ, UK
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Lee D, Rushworth A, Sahu SK. A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution. Biometrics 2014; 70:419-29. [PMID: 24571082 PMCID: PMC4282098 DOI: 10.1111/biom.12156] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Revised: 01/01/2014] [Accepted: 01/01/2014] [Indexed: 11/28/2022]
Abstract
Estimation of the long-term health effects of air pollution is a challenging task, especially when modeling spatial small-area disease incidence data in an ecological study design. The challenge comes from the unobserved underlying spatial autocorrelation structure in these data, which is accounted for using random effects modeled by a globally smooth conditional autoregressive model. These smooth random effects confound the effects of air pollution, which are also globally smooth. To avoid this collinearity a Bayesian localized conditional autoregressive model is developed for the random effects. This localized model is flexible spatially, in the sense that it is not only able to model areas of spatial smoothness, but also it is able to capture step changes in the random effects surface. This methodological development allows us to improve the estimation performance of the covariate effects, compared to using traditional conditional auto-regressive models. These results are established using a simulation study, and are then illustrated with our motivating study on air pollution and respiratory ill health in Greater Glasgow, Scotland in 2011. The model shows substantial health effects of particulate matter air pollution and nitrogen dioxide, whose effects have been consistently attenuated by the currently available globally smooth models.
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Affiliation(s)
- Duncan Lee
- School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8QW, UK
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Lee D, Mitchell R. Locally adaptive spatial smoothing using conditional auto-regressive models. J R Stat Soc Ser C Appl Stat 2013. [DOI: 10.1111/rssc.12009] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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