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Etxeberria J, Goicoa T, Ugarte MD. Using mortality to predict incidence for rare and lethal cancers in very small areas. Biom J 2023; 65:e2200017. [PMID: 36180401 DOI: 10.1002/bimj.202200017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 06/17/2022] [Accepted: 08/14/2022] [Indexed: 11/11/2022]
Abstract
Incidence and mortality figures are needed to get a comprehensive overview of cancer burden. In many countries, cancer mortality figures are routinely recorded by statistical offices, whereas incidence depends on regional cancer registries. However, due to the complexity of updating cancer registries, incidence numbers become available 3 or 4 years later than mortality figures. It is, therefore, necessary to develop reliable procedures to predict cancer incidence at least until the period when mortality data are available. Most of the methods proposed in the literature are designed to predict total cancer (except nonmelanoma skin cancer) or major cancer sites. However, less frequent lethal cancers, such as brain cancer, are generally excluded from predictions because the scarce number of cases makes it difficult to use univariate models. Our proposal comes to fill this gap and consists of modeling jointly incidence and mortality data using spatio-temporal models with spatial and age shared components. This approach allows for predicting lethal cancers improving the performance of individual models when data are scarce by taking advantage of the high correlation between incidence and mortality. A fully Bayesian approach based on integrated nested Laplace approximations is considered for model fitting and inference. A validation process is also conducted to assess the performance of alternative models. We use the new proposals to predict brain cancer incidence rates by gender and age groups in the health units of Navarre and Basque Country (Spain) during the period 2005-2008.
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Affiliation(s)
- Jaione Etxeberria
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre (UPNA), Campus Arrosadia, Pamplona, Navarre, Spain
- Institute for Advanced Materials and Mathematics (INAMAT2), Public University of Navarre (UPNA), Campus Arrosadia, Pamplona, Navarre, Spain
| | - Tomás Goicoa
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre (UPNA), Campus Arrosadia, Pamplona, Navarre, Spain
- Institute for Advanced Materials and Mathematics (INAMAT2), Public University of Navarre (UPNA), Campus Arrosadia, Pamplona, Navarre, Spain
- Research Network on Health Services in Chronic Diseases (REDISSEC), Madrid, Spain
| | - Maria D Ugarte
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre (UPNA), Campus Arrosadia, Pamplona, Navarre, Spain
- Institute for Advanced Materials and Mathematics (INAMAT2), Public University of Navarre (UPNA), Campus Arrosadia, Pamplona, Navarre, Spain
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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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Jaya IGNM, Folmer H. Bayesian spatiotemporal forecasting and mapping of COVID-19 risk with application to West Java Province, Indonesia. J Reg Sci 2021; 61:849-881. [PMID: 34230688 PMCID: PMC8250786 DOI: 10.1111/jors.12533] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 01/30/2021] [Accepted: 03/26/2021] [Indexed: 05/16/2023]
Abstract
The coronavirus disease (COVID-19) has spread rapidly to multiple countries including Indonesia. Mapping its spatiotemporal pattern and forecasting (small area) outbreaks are crucial for containment and mitigation strategies. Hence, we introduce a parsimonious space-time model of new infections that yields accurate forecasts but only requires information regarding the number of incidences and population size per geographical unit and time period. Model parsimony is important because of limited knowledge regarding the causes of COVID-19 and the need for rapid action to control outbreaks. We outline the basics of Bayesian estimation, forecasting, and mapping, in particular for the identification of hotspots. The methodology is applied to county-level data of West Java Province, Indonesia.
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Affiliation(s)
- I. Gede Nyoman M. Jaya
- Department of Economic Geography, Faculty of Spatial SciencesGroningen UniversityGroningenThe Netherlands
- Department of StatisticsPadjadjaran UniversityBandungIndonesia
| | - Henk Folmer
- Department of Economic Geography, Faculty of Spatial SciencesGroningen UniversityGroningenThe Netherlands
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Perez-Panades J, Botella-Rocamora P, Martinez-Beneito MA. Beyond standardized mortality ratios; some uses of smoothed age-specific mortality rates on small areas studies. Int J Health Geogr 2020; 19:54. [PMID: 33276785 PMCID: PMC7716592 DOI: 10.1186/s12942-020-00251-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 11/19/2020] [Indexed: 11/30/2022] Open
Abstract
Background Most epidemiological risk indicators strongly depend on the age composition of populations, which makes the direct comparison of raw (unstandardized) indicators misleading because of the different age structures of the spatial units of study. Age-standardized rates (ASR) are a common solution for overcoming this confusing effect. The main drawback of ASRs is that they depend on age-specific rates which, when working with small areas, are often based on very few, or no, observed cases for most age groups. A similar effect occurs with life expectancy at birth and many more epidemiological indicators, which makes standardized mortality ratios (SMR) the omnipresent risk indicator for small areas epidemiologic studies. Methods To deal with this issue, a multivariate smoothing model, the M-model, is proposed in order to fit the age-specific probabilities of death (PoDs) for each spatial unit, which assumes dependence between closer age groups and spatial units. This age–space dependence structure enables information to be transferred between neighboring consecutive age groups and neighboring areas, at the same time, providing more reliable age-specific PoDs estimates. Results Three case studies are presented to illustrate the wide range of applications that smoothed age specific PoDs have in practice . The first case study shows the application of the model to a geographical study of lung cancer mortality in women. This study illustrates the convenience of considering age–space interactions in geographical studies and to explore the different spatial risk patterns shown by the different age groups. Second, the model is also applied to the study of ischaemic heart disease mortality in women in two cities at the census tract level. Smoothed age-standardized rates are derived and compared for the census tracts of both cities, illustrating some advantages of this mortality indicator over traditional SMRs. In the latest case study, the model is applied to estimate smoothed life expectancy (LE), which is the most widely used synthetic indicator for characterizing overall mortality differences when (not so small) spatial units are considered. Conclusion Our age–space model is an appropriate and flexible proposal that provides more reliable estimates of the probabilities of death, which allow the calculation of enhanced epidemiological indicators (smoothed ASR, smoothed LE), thus providing alternatives to traditional SMR-based studies of small areas.
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Affiliation(s)
- Jordi Perez-Panades
- Direcció General de Salut Pública i Addiccions, Conselleria de Sanitat Universal i Salut Pública, Avda/Cataluña, 21, 46020, Valencia, Spain.
| | - Paloma Botella-Rocamora
- Direcció General de Salut Pública i Addiccions, Conselleria de Sanitat Universal i Salut Pública, Avda/Cataluña, 21, 46020, Valencia, Spain
| | - Miguel Angel Martinez-Beneito
- Departament d'Estadística i Investigació Operativa, Universitat de València, C/Dr. Moliner, 50, 46100, Burjassot, Valencia, Spain
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Blangiardo M, Boulieri A, Diggle P, Piel FB, Shaddick G, Elliott P. Advances in spatiotemporal models for non-communicable disease surveillance. Int J Epidemiol 2020; 49 Suppl 1:i26-i37. [PMID: 32293008 PMCID: PMC7158067 DOI: 10.1093/ije/dyz181] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 08/07/2019] [Indexed: 12/03/2022] Open
Abstract
Surveillance systems are commonly used to provide early warning detection or to assess an impact of an intervention/policy. Traditionally, the methodological and conceptual frameworks for surveillance have been designed for infectious diseases, but the rising burden of non-communicable diseases (NCDs) worldwide suggests a pressing need for surveillance strategies to detect unusual patterns in the data and to help unveil important risk factors in this setting. Surveillance methods need to be able to detect meaningful departures from expectation and exploit dependencies within such data to produce unbiased estimates of risk as well as future forecasts. This has led to the increasing development of a range of space-time methods specifically designed for NCD surveillance. We present an overview of recent advances in spatiotemporal disease surveillance for NCDs, using hierarchically specified models. This provides a coherent framework for modelling complex data structures, dealing with data sparsity, exploiting dependencies between data sources and propagating the inherent uncertainties present in both the data and the modelling process. We then focus on three commonly used models within the Bayesian Hierarchical Model (BHM) framework and, through a simulation study, we compare their performance. We also discuss some challenges faced by researchers when dealing with NCD surveillance, including how to account for false detection and the modifiable areal unit problem. Finally, we consider how to use and interpret the complex models, how model selection may vary depending on the intended user group and how best to communicate results to stakeholders and the general public.
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Affiliation(s)
- Marta Blangiardo
- UK Small Area Health Statistics Unit
- MRC-PHE Centre for Environment & Health, Department of Epidemiology & Biostatistics, Imperial College London, London, UK
| | - Areti Boulieri
- MRC-PHE Centre for Environment & Health, Department of Epidemiology & Biostatistics, Imperial College London, London, UK
| | - Peter Diggle
- Centre for Health Informatics, Computing and Statistics (CHICAS), Lancaster Medical School, Lancaster University, Lancaster, UK
| | - Frédéric B Piel
- UK Small Area Health Statistics Unit
- MRC-PHE Centre for Environment & Health, Department of Epidemiology & Biostatistics, Imperial College London, London, UK
| | - Gavin Shaddick
- Department of Mathematics, University of Exeter, Exeter, UK
| | - Paul Elliott
- UK Small Area Health Statistics Unit
- MRC-PHE Centre for Environment & Health, Department of Epidemiology & Biostatistics, Imperial College London, London, UK
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Peluso S, Mira A, Rue H, Tierney NJ, Benvenuti C, Cianella R, Caputo ML, Auricchio A. A Bayesian spatiotemporal statistical analysis of out-of-hospital cardiac arrests. Biom J 2020; 62:1105-1119. [PMID: 32011763 DOI: 10.1002/bimj.201900166] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 11/21/2019] [Accepted: 12/16/2019] [Indexed: 11/08/2022]
Abstract
We propose a Bayesian spatiotemporal statistical model for predicting out-of-hospital cardiac arrests (OHCAs). Risk maps for Ticino, adjusted for demographic covariates, are built for explaining and forecasting the spatial distribution of OHCAs and their temporal dynamics. The occurrence intensity of the OHCA event in each area of interest, and the cardiac risk-based clustering of municipalities are efficiently estimated, through a statistical model that decomposes OHCA intensity into overall intensity, demographic fixed effects, spatially structured and unstructured random effects, time polynomial dependence, and spatiotemporal random effect. In the studied geography, time evolution and dependence on demographic features are robust over different categories of OHCAs, but with variability in their spatial and spatiotemporal structure. Two main OHCA incidence-based clusters of municipalities are identified.
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Affiliation(s)
- Stefano Peluso
- Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan, Italy
| | - Antonietta Mira
- Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland.,Department of Science and High Technology, Università degli Studi dell'Insubria, Como, Italy
| | - Håvard Rue
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | | | | | - Roberto Cianella
- FCTSA Federazione Cantonale Ticinese Servizi Autoambulanze, Switzerland
| | - Maria Luce Caputo
- Fondazione Cardiocentro Ticino, Division of Cardiology, Lugano, Switzerland.,Department of Molecular Medicine, University of Pavia, Pavia, Italy
| | - Angelo Auricchio
- Fondazione Ticino Cuore, Breganzona, Switzerland.,Fondazione Cardiocentro Ticino, Division of Cardiology, Lugano, Switzerland.,Center for Computational Medicine in Cardiology, Università della Svizzera italiana, Lugano, Switzerland
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Adin A, Goicoa T, Ugarte MD. Online relative risks/rates estimation in spatial and spatio-temporal disease mapping. Comput Methods Programs Biomed 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Goicoa T, Adin A, Etxeberria J, Militino AF, Ugarte MD. Flexible Bayesian P-splines for smoothing age-specific spatio-temporal mortality patterns. Stat Methods Med Res 2017; 28:384-403. [PMID: 28847210 DOI: 10.1177/0962280217726802] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
In this paper age-space-time models based on one and two-dimensional P-splines with B-spline bases are proposed for smoothing mortality rates, where both fixed relative scale and scale invariant two-dimensional penalties are examined. Model fitting and inference are carried out using integrated nested Laplace approximations, a recent Bayesian technique that speeds up computations compared to McMC methods. The models will be illustrated with Spanish breast cancer mortality data during the period 1985-2010, where a general decline in breast cancer mortality has been observed in Spanish provinces in the last decades. The results reveal that mortality rates for the oldest age groups do not decrease in all provinces.
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Affiliation(s)
- T Goicoa
- 1 Department of Statistics and Operations Research, Public University of Navarre, Spain.,2 Institute for Advanced Materials (InaMat), Public University of Navarre, Spain.,3 Research Network on Health Services in Chronic Diseases (REDISSEC), Spain
| | - A Adin
- 1 Department of Statistics and Operations Research, Public University of Navarre, Spain.,2 Institute for Advanced Materials (InaMat), Public University of Navarre, Spain
| | - J Etxeberria
- 1 Department of Statistics and Operations Research, Public University of Navarre, Spain.,2 Institute for Advanced Materials (InaMat), Public University of Navarre, Spain.,4 Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Spain
| | - A F Militino
- 1 Department of Statistics and Operations Research, Public University of Navarre, Spain.,2 Institute for Advanced Materials (InaMat), Public University of Navarre, Spain
| | - M D Ugarte
- 1 Department of Statistics and Operations Research, Public University of Navarre, Spain.,2 Institute for Advanced Materials (InaMat), Public University of Navarre, Spain
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