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Satorra P, Tebé C. Bayesian spatio-temporal analysis of the COVID-19 pandemic in Catalonia. Sci Rep 2024; 14:4220. [PMID: 38378913 PMCID: PMC10879174 DOI: 10.1038/s41598-024-53527-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/31/2024] [Indexed: 02/22/2024] Open
Abstract
In this study, we modelled the incidence of COVID-19 cases and hospitalisations by basic health areas (ABS) in Catalonia. Spatial, temporal and spatio-temporal incidence trends were described using estimation methods that allow to borrow strength from neighbouring areas and time points. Specifically, we used Bayesian hierarchical spatio-temporal models estimated with Integrated Nested Laplace Approximation (INLA). An exploratory analysis was conducted to identify potential ABS factors associated with the incidence of cases and hospitalisations. High heterogeneity in cases and hospitalisation incidence was found between ABS and along the waves of the pandemic. Urban areas were found to have a higher incidence of COVID-19 cases and hospitalisations than rural areas, while socio-economic deprivation of the area was associated with a higher incidence of hospitalisations. In addition, full vaccination coverage in each ABS showed a protective effect on the risk of COVID-19 cases and hospitalisations.
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Affiliation(s)
- Pau Satorra
- Biostatistics Support and Research Unit, Germans Trias i Pujol Research Institute and Hospital (IGTP), Badalona, Barcelona, Spain
| | - Cristian Tebé
- Biostatistics Support and Research Unit, Germans Trias i Pujol Research Institute and Hospital (IGTP), Badalona, Barcelona, Spain.
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2
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Orozco-Acosta E, Riebler A, Adin A, Ugarte MD. A scalable approach for short-term disease forecasting in high spatial resolution areal data. Biom J 2023; 65:e2300096. [PMID: 37890279 DOI: 10.1002/bimj.202300096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 08/21/2023] [Accepted: 08/30/2023] [Indexed: 10/29/2023]
Abstract
Short-term disease forecasting at specific discrete spatial resolutions has become a high-impact decision-support tool in health planning. However, when the number of areas is very large obtaining predictions can be computationally intensive or even unfeasible using standard spatiotemporal models. The purpose of this paper is to provide a method for short-term predictions in high-dimensional areal data based on a newly proposed "divide-and-conquer" approach. We assess the predictive performance of this method and other classical spatiotemporal models in a validation study that uses cancer mortality data for the 7907 municipalities of continental Spain. The new proposal outperforms traditional models in terms of mean absolute error, root mean square error, and interval score when forecasting cancer mortality 1, 2, and 3 years ahead. Models are implemented in a fully Bayesian framework using the well-known integrated nested Laplace estimation technique.
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Affiliation(s)
- Erick Orozco-Acosta
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
- Institute for Advanced Materials and Mathematics, InaMat2, Public University of Navarre, Pamplona, Spain
| | - Andrea Riebler
- Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Aritz Adin
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
- Institute for Advanced Materials and Mathematics, InaMat2, Public University of Navarre, Pamplona, Spain
| | - Maria D Ugarte
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
- Institute for Advanced Materials and Mathematics, InaMat2, Public University of Navarre, Pamplona, Spain
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Pavani J, Bastos LS, Moraga P. Joint spatial modeling of the risks of co-circulating mosquito-borne diseases in Ceará, Brazil. Spat Spatiotemporal Epidemiol 2023; 47:100616. [PMID: 38042535 DOI: 10.1016/j.sste.2023.100616] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 08/06/2023] [Accepted: 08/18/2023] [Indexed: 12/04/2023]
Abstract
Mosquito-borne diseases such as dengue and chikungunya have been co-circulating in the Americas, causing great damage to the population. In 2021, for instance, almost 1.5 million cases were reported on the continent, being Brazil the responsible for most of them. Even though they are transmitted by the same mosquito, it remains unclear whether there exists a relationship between both diseases. In this paper, we model the geographic distributions of dengue and chikungunya over the years 2016 to 2021 in the Brazilian state of Ceará. We use a Bayesian hierarchical spatial model for the joint analysis of two arboviruses that includes spatial covariates as well as specific and shared spatial effects that take into account the potential autocorrelation between the two diseases. Our findings allow us to identify areas with high risk of one or both diseases. Only 7% of the areas present high relative risk for both diseases, which suggests a competition between viruses. This study advances the understanding of the geographic patterns and the identification of risk factors of dengue and chikungunya being able to help health decision-making.
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Affiliation(s)
- Jessica Pavani
- Department of Statistics, Pontificia Universidad Católica de Chile, Santiago, Chile.
| | - Leonardo S Bastos
- Scientific Computing Program, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Paula Moraga
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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4
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Song C, Yin H, Shi X, Xie M, Yang S, Zhou J, Wang X, Tang Z, Yang Y, Pan J. Spatiotemporal disparities in regional public risk perception of COVID-19 using Bayesian Spatiotemporally Varying Coefficients (STVC) series models across Chinese cities. INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION : IJDRR 2022; 77:103078. [PMID: 35664453 PMCID: PMC9148270 DOI: 10.1016/j.ijdrr.2022.103078] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 05/24/2022] [Accepted: 05/24/2022] [Indexed: 05/11/2023]
Abstract
Regional public attention has been critical during the COVID-19 pandemic, impacting the effectiveness of sub-national non-pharmaceutical interventions. While studies have focused on public attention at the national level, sub-national public attention has not been well investigated. Understanding sub-national public attention can aid local governments in designing regional scientific guidelines, especially in large countries with substantial spatiotemporal disparities in the spread of infections. Here, we evaluated the online public attention to the COVID-19 pandemic using internet search data and developed a regional public risk perception index (PRPI) that depicts heterogeneous associations between local pandemic risk and public attention across 366 Chinese cities. We used the Bayesian Spatiotemporally Varying Coefficients (STVC) model, a full-map local regression for estimating spatiotemporal heterogeneous relationships of variables, and improved it to the Bayesian Spatiotemporally Interacting Varying Coefficients (STIVC) model to incorporate space-time interaction non-stationarity at spatial or temporal stratified scales. COVID-19 daily cases (median contribution 82.6%) was the most critical factor affecting public attention, followed by urban socioeconomic conditions (16.7%) and daily population mobility (0.7%). After adjusting national and provincial impacts, city-level influence factors accounted for 89.4% and 58.6% in spatiotemporal variations of public attention. Spatiotemporal disparities were substantial among cities and provinces, suggesting that observing national-level public dynamics alone was insufficient. Multi-period PRPI maps revealed clusters and outlier cities with potential public panic and low health literacy. Bayesian STVC series models are systematically proposed and provide a multi-level spatiotemporal heterogeneous analytical framework for understanding collective human responses to major public health emergencies and disasters.
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Affiliation(s)
- Chao Song
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610044, China
- Department of Geography, Dartmouth College, Hanover, NH, 03755, USA
- Institute for Healthy Cities and West China Research Centre for Rural Health Development, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Hao Yin
- Department of Economics, University of Southern California, CA, 90089, USA
- School of Population and Public Health, University of British Columbia, BC, V6T 1Z3, Canada
| | - Xun Shi
- Department of Geography, Dartmouth College, Hanover, NH, 03755, USA
| | - Mingyu Xie
- Institute for Healthy Cities and West China Research Centre for Rural Health Development, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Shujuan Yang
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610044, China
| | - Junmin Zhou
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610044, China
| | - Xiuli Wang
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610044, China
- Institute for Healthy Cities and West China Research Centre for Rural Health Development, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Zhangying Tang
- State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, School of Geoscience and Technology, Southwest Petroleum University, Chengdu, Sichuan, 610500, China
| | - Yili Yang
- Institute for Healthy Cities and West China Research Centre for Rural Health Development, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Jay Pan
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610044, China
- Institute for Healthy Cities and West China Research Centre for Rural Health Development, Sichuan University, Chengdu, Sichuan, 610041, China
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Kelling C, Graif C, Korkmaz G, Haran M. Modeling the Social and Spatial Proximity of Crime: Domestic and Sexual Violence Across Neighborhoods. JOURNAL OF QUANTITATIVE CRIMINOLOGY 2021; 37:481-516. [PMID: 34149156 PMCID: PMC8210633 DOI: 10.1007/s10940-020-09454-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
OBJECTIVES Our goal is to understand the social dynamics affecting domestic and sexual violence in urban areas by investigating the role of connections between area nodes, or communities. We use innovative methods adapted from spatial statistics to investigate the importance of social proximity measured based on connectedness pathways between area nodes. In doing so, we seek to extend the standard treatment in the neighborhoods and crime literature of areas like census blocks as independent analytical units or as interdependent primarily due to geographic proximity. METHODS In this paper, we develop techniques to incorporate two types of proximity, geographic proximity and commuting proximity in spatial generalized linear mixed models (SGLMM) in order to estimate domestic and sexual violence in Detroit, Michigan and Arlington County, Virginia. Analyses are based on three types of CAR models (the Besag, York, and Mollié (BYM), Leroux, and the sparse SGLMM models) and two types of SAR models (the spatial lag and spatial error models) to examine how results vary with different model assumptions. We use data from local and federal sources such as the Police Data Initiative and American Community Survey. RESULTS Analyses show that incorporating information on commuting ties, a non-spatially bounded form of social proximity, to spatial models contributes to better deviance information criteria (DIC) scores (a metric which explicitly accounts for model fit and complexity) in Arlington for sexual and domestic crime as well as overall crime. In Detroit, the fit is improved only for overall crime. The distinctions in model fit are less pronounced when using cross-validated mean absolute error (MAE) as a comparison criteria. CONCLUSION Overall, the results indicate variations across crime type, urban contexts, and modeling approaches. Nonetheless, in important contexts, commuting ties among neighborhoods are observed to greatly improve our understanding of urban crime. If such ties contribute to the transfer of norms, social support, resources, and behaviors between places, they may then transfer also the effects of crime prevention efforts.
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Affiliation(s)
- Claire Kelling
- 330B Thomas Building, University Park, PA 16802
- Department of Statistics, Pennsylvania State University, University Park, PA
| | - Corina Graif
- Department of Sociology and Criminology, Pennsylvania State University, University Park, PA
| | - Gizem Korkmaz
- Biocomplexity Institute & Initiative, University of Virginia, 1100 Wilson Blvd., Arlington, VA
| | - Murali Haran
- Department of Statistics, Pennsylvania State University, University Park, PA
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Wah W, Ahern S, Earnest A. A systematic review of Bayesian spatial-temporal models on cancer incidence and mortality. Int J Public Health 2020; 65:673-682. [PMID: 32449006 DOI: 10.1007/s00038-020-01384-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 04/26/2020] [Accepted: 05/02/2020] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVES This study aimed to review the types and applications of fully Bayesian (FB) spatial-temporal models and covariates used to study cancer incidence and mortality. METHODS This systematic review searched articles published within Medline, Embase, Web-of-Science and Google Scholar between 2014 and 2018. RESULTS A total of 38 studies were included in our study. All studies applied Bayesian spatial-temporal models to explore spatial patterns over time, and over half assessed the association with risk factors. Studies used different modelling approaches and prior distributions for spatial, temporal and spatial-temporal interaction effects depending on the nature of data, outcomes and applications. The most common Bayesian spatial-temporal model was a generalized linear mixed model. These models adjusted for covariates at the patient, area or temporal level, and through standardization. CONCLUSIONS Few studies (4) modelled patient-level clinical characteristics (11%), and the applications of an FB approach in the forecasting of spatial-temporally aligned cancer data were limited. This review highlighted the need for Bayesian spatial-temporal models to incorporate patient-level prognostic characteristics through the multi-level framework and forecast future cancer incidence and outcomes for cancer prevention and control strategies.
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Affiliation(s)
- Win Wah
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
| | - Susannah Ahern
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Arul Earnest
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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Adin A, Goicoa T, Ugarte MD. Online relative risks/rates estimation in spatial and spatio-temporal disease mapping. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 172:103-116. [PMID: 30846296 DOI: 10.1016/j.cmpb.2019.02.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 02/13/2019] [Accepted: 02/25/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Spatial and spatio-temporal analyses of count data are crucial in epidemiology and other fields to unveil spatial and spatio-temporal patterns of incidence and/or mortality risks. However, fitting spatial and spatio-temporal models is not easy for non-expert users. The objective of this paper is to present an interactive and user-friendly web application (named SSTCDapp) for the analysis of spatial and spatio-temporal mortality or incidence data. Although SSTCDapp is simple to use, the underlying statistical theory is well founded and all key issues such as model identifiability, model selection, and several spatial priors and hyperpriors for sensitivity analyses are properly addressed. METHODS The web application is designed to fit an extensive range of fairly complex spatio-temporal models to smooth the very often extremely variable standardized incidence/mortality risks or crude rates. The application is built with the R package shiny and relies on the well founded integrated nested Laplace approximation technique for model fitting and inference. RESULTS The use of the web application is shown through the analysis of Spanish spatio-temporal breast cancer data. Different possibilities for the analysis regarding the type of model, model selection criteria, and a range of graphical as well as numerical outputs are provided. CONCLUSIONS Unlike other software used in disease mapping, SSTCDapp facilitates the fit of complex statistical models to non-experts users without the need of installing any software in their own computers, since all the analyses and computations are made in a powerful remote server. In addition, a desktop version is also available to run the application locally in those cases in which data confidentiality is a serious issue.
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Affiliation(s)
- Aritz Adin
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain; InaMAT, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain.
| | - Tomás Goicoa
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain; InaMAT, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain.
| | - María Dolores Ugarte
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain; InaMAT, Public University of Navarre, Campus de Arrosadia, Pamplona 31006, Spain.
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Adin A, Martínez-Bello DA, López-Quílez A, Ugarte MD. Two-level resolution of relative risk of dengue disease in a hyperendemic city of Colombia. PLoS One 2018; 13:e0203382. [PMID: 30204762 PMCID: PMC6133285 DOI: 10.1371/journal.pone.0203382] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Accepted: 08/20/2018] [Indexed: 01/25/2023] Open
Abstract
Risk maps of dengue disease offer to the public health officers a tool to model disease risk in space and time. We analyzed the geographical distribution of relative incidence risk of dengue disease in a high incidence city from Colombia, and its evolution in time during the period January 2009—December 2015, identifying regional effects at different levels of spatial aggregations. Cases of dengue disease were geocoded and spatially allocated to census sectors, and temporally aggregated by epidemiological periods. The census sectors are nested in administrative divisions defined as communes, configuring two levels of spatial aggregation for the dengue cases. Spatio-temporal models including census sector and commune-level spatially structured random effects were fitted to estimate dengue incidence relative risks using the integrated nested Laplace approximation (INLA) technique. The final selected model included two-level spatial random effects, a global structured temporal random effect, and a census sector-level interaction term. Risk maps by epidemiological period and risk profiles by census sector were generated from the modeling process, showing the transmission dynamics of the disease. All the census sectors in the city displayed high risk at some epidemiological period in the outbreak periods. Relative risk estimation of dengue disease using INLA offered a quick and powerful method for parameter estimation and inference.
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Affiliation(s)
- Aritz Adin
- Department of Statistics, Computer Science, and Mathematics, Public University of Navarre, Spain
- Institute for Advanced Materials (InaMat), Public University of Navarre, Spain
| | - Daniel Adyro Martínez-Bello
- Departament d’Estadística i Investigació Operativa, Facultat de Matemàtiques, Universitat de València, València, Spain
| | - Antonio López-Quílez
- Departament d’Estadística i Investigació Operativa, Facultat de Matemàtiques, Universitat de València, València, Spain
| | - María Dolores Ugarte
- Department of Statistics, Computer Science, and Mathematics, Public University of Navarre, Spain
- Institute for Advanced Materials (InaMat), Public University of Navarre, Spain
- * E-mail:
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Martínez-Bello DA, López-Quílez A, Torres Prieto A. Spatio-Temporal Modeling of Zika and Dengue Infections within Colombia. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:ijerph15071376. [PMID: 29966348 PMCID: PMC6068969 DOI: 10.3390/ijerph15071376] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 06/23/2018] [Accepted: 06/26/2018] [Indexed: 12/14/2022]
Abstract
The aim of this study is to estimate the parallel relative risk of Zika virus disease (ZVD) and dengue using spatio-temporal interaction effects models for one department and one city of Colombia during the 2015–2016 ZVD outbreak. We apply the integrated nested Laplace approximation (INLA) for parameter estimation, using the epidemiological week (EW) as a time measure. At the departmental level, the best model showed that the dengue or ZVD risk in one municipality was highly associated with risk in the same municipality during the preceding EWs, while at the city level, the final model selected established that the high risk of dengue or ZVD in one census sector was highly associated not only with its neighboring census sectors in the same EW, but also with its neighboring sectors in the preceding EW. The spatio-temporal models provided smoothed risk estimates, credible risk intervals, and estimation of the probability of high risk of dengue and ZVD by area and time period. We explore the intricacies of the modeling process and interpretation of the results, advocating for the use of spatio-temporal models of the relative risk of dengue and ZVD in order to generate highly valuable epidemiological information for public health decision making.
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Affiliation(s)
- Daniel Adyro Martínez-Bello
- Department of Statistics and Operations Research, Faculty of Mathematics, University of Valencia, 46100 Valencia, Spain.
| | - Antonio López-Quílez
- Department of Statistics and Operations Research, Faculty of Mathematics, University of Valencia, 46100 Valencia, Spain.
| | - Alexander Torres Prieto
- Epidemiologic Monitoring Office, Secretary of Health of the Department of Santander, Cl. 45 11-52 Bucaramanga, Colombia.
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Adin A, Lee D, Goicoa T, Ugarte MD. A two-stage approach to estimate spatial and spatio-temporal disease risks in the presence of local discontinuities and clusters. Stat Methods Med Res 2018; 28:2595-2613. [DOI: 10.1177/0962280218767975] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Disease risk maps for areal unit data are often estimated from Poisson mixed models with local spatial smoothing, for example by incorporating random effects with a conditional autoregressive prior distribution. However, one of the limitations is that local discontinuities in the spatial pattern are not usually modelled, leading to over-smoothing of the risk maps and a masking of clusters of hot/coldspot areas. In this paper, we propose a novel two-stage approach to estimate and map disease risk in the presence of such local discontinuities and clusters. We propose approaches in both spatial and spatio-temporal domains, where for the latter the clusters can either be fixed or allowed to vary over time. In the first stage, we apply an agglomerative hierarchical clustering algorithm to training data to provide sets of potential clusters, and in the second stage, a two-level spatial or spatio-temporal model is applied to each potential cluster configuration. The superiority of the proposed approach with regard to a previous proposal is shown by simulation, and the methodology is applied to two important public health problems in Spain, namely stomach cancer mortality across Spain and brain cancer incidence in the Navarre and Basque Country regions of Spain.
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Affiliation(s)
- A Adin
- Department of Statistics and O. R., Public University of Navarre, Navarra, Spain
- Institute for Advanced Materials (InaMat), Public University of Navarre, Navarra, Spain
| | - D Lee
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - T Goicoa
- Department of Statistics and O. R., Public University of Navarre, Navarra, Spain
- Institute for Advanced Materials (InaMat), Public University of Navarre, Navarra, Spain
- Research Network on Health Services in Chronic Diseases (REDISSEC), Madrid, Spain
| | - María Dolores Ugarte
- Department of Statistics and O. R., Public University of Navarre, Navarra, Spain
- Institute for Advanced Materials (InaMat), Public University of Navarre, Navarra, Spain
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