Open Access
Hierarchical and spline-based models in space-time disease mapping
(2017) Adin Urtasun, Aritz; Ugarte Martínez, María Dolores; Goicoa Mangado, Tomás; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa
La representación cartográfica de enfermedades (disease mapping) es un área de investigación de gran interés en epidemiología y salud pública. La gran variabilidad inherente a las medidas clásicas de estimación de riesgo como la razón de mortalidad estandarizada, hacen necesario el uso de técnicas estadísticas que estabilicen estas razones. Durante los últimos años se han desarrollado muchos modelos estadísticos para estudiar la distribución geográfica de una enfermedad y su evolución en el tiempo. Sin embargo, la disponibilidad de datos de alta calidad recogidos en muchas regiones y durante largos periodos de tiempo, así como la aparición de nuevos y cada vez más sofisticados modelos, han revelado nuevas dificultades que necesitan ser investigadas a fondo. En el Capítulo 1 se describen algunos modelos espacio-temporales de relevancia para el resto de capítulos abordados en la tesis y se detallan las restricciones necesarias para resolver los problemas de identificación de dichos modelos. El Capítulo 1 también describe la técnica inferencia! Bayesiana utilizada a lo largo de la tesis, basada en aproximaciones de Laplace e integración numérica (conocida como INLA), y su implementación en R. En el Capítulo 2 se han comparado cinco modelos espacio-temporales utilizados en disease mapping. Para poder comparar los diferentes términos de estos modelos, se ha calculado una descomposición del logaritmo de los riesgos estimados definiendo patrones espaciales, temporales y espacio-temporales a posteriori. Los resultados se ilustran con datos de mortalidad por cáncer de encéfalo en las provincias Españolas durante el periodo 1986-2010. Además, se ha realizado un estudio de simulación para comparar el rendimiento de los modelos en términos de sensitividad (habilidad para detectar regiones de alto riesgo verdaderas) y especificidad (habilidad para descartar regiones de alto riesgo falsas). Se concluye que cuando el número de casos esperados es muy pequeño (algo común cuando se analizan enfermedades raras o dominios muy pequeños como municipios), los modelos de P-splines se comportan mejor en términos de detección de áreas de alto riesgo. En el Capítulo 3 se propone una nueva familia de modelos espacio-temporales que incluyen efectos aleatorios para dos niveles espaciales, permitiendo modelizar efectos espaciales y espacio-temporales a diferentes niveles de agregación (como por ejemplo, municipios dentro de provincias o zonas de salud que se ven afectados por políticas de salud similares). Estos modelos han sido utilizados para analizar los datos de mortalidad en los municipios del País Vasco y Navarra durante el periodo 1986-2008. Se ha realizado un estudio de simulación en donde se concluye que si existen diferentes niveles de agregación espacial, los nuevos modelos a dos niveles se comportan mejor que modelos previos propuestos en la literatura. En el Capítulo 4 se presentan nuevos modelos de E-splines que incluyen correlaciones espaciales y temporales desde un enfoque completamente Bayesiano. Concretamente se describen modelos que incluyen B-spline temporales unidimensionales que pueden tener (o no) correlación espacial, así como modelos de B-spline espaciales bidimensionales que pueden tener (o no) correlación temporal. Los resultados se ilustran con datos de mortalidad por cáncer de mama en la España peninsular durante el periodo 1990-2010. Se observa que, en general, utilizar modelos con B-spline temporales distintos para cada área proporciona mejores resultados en términos de ajuste. Sin embargo, cuando el número de áreas aumenta, estos modelos pueden no ser factibles desde un punto de vista computacional. Por el contrario, los modelos de P-spline tridimensionales (previamente propuestos en la literatura y formulados en esta tesis desde un punto de vista completamente Bayesiano) son una alternativa prometedora, obteniendo estimaciones del riesgo precisas en tiempos computaciones mucho más cortos.
Open Access
Online relative risks/rates estimation in spatial and spatio-temporal disease mapping
(Elsevier, 2019) Adin Urtasun, Aritz; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas
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.
Open Access
Temporal evolution of brain cancer incidence in the municipalities of Navarre and the Basque Country, Spain
(BioMed Central, 2015) Ugarte Martínez, María Dolores; Adin Urtasun, Aritz; Goicoa Mangado, Tomás; Casado, Itziar; Ardanaz, Eva; Larrañaga, Nerea; Estatistika eta Ikerketa Operatiboa; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística e Investigación Operativa; Gobierno de Navarra / Nafarroako Gobernua: proyecto 113 Res. 2186/2014
Background: Brain cancer incidence rates in Spain are below the European’s average. However, there are two regions in the north of the country, Navarre and the Basque Country, ranked among the European regions with the highest incidence rates for both males and females. Our objective here was two-fold. Firstly, to describe the temporal evolution of the geographical pattern of brain cancer incidence in Navarre and the Basque Country, and secondly, to look for specific high risk areas (municipalities) within these two regions in the study period (1986–2008). Methods: A mixed Poisson model with two levels of spatial effects is used. The model also included two levels of spatial effects (municipalities and local health areas). Model fitting was carried out using penalized quasi-likelihood. High risk regions were detected using upper one-sided confidence intervals. Results: Results revealed a group of high risk areas surrounding Pamplona, the capital city of Navarre, and a few municipalities with significant high risks in the northern part of the region, specifically in the border between Navarre and the Basque Country (Gipuzkoa). The global temporal trend was found to be increasing. Differences were also observed among specific risk evolutions in certain municipalities. Conclusions: Brain cancer incidence in Navarre and the Basque Country (Spain) is still increasing with time. The number of high risk areas within those two regions is also increasing. Our study highlights the need of continuous surveillance of this cancer in the areas of high risk. However, due to the low percentage of cases explained by the known risk factors, primary prevention should be applied as a general recommendation in these populations.
Open Access
A two-stage approach to estimate spatial and spatio-temporal disease risks in the presence of local discontinuities and clusters
(SAGE, 2018-04-13) Adin Urtasun, Aritz; Lee, Duncan; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2
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.
Open Access
In spatio-temporal disease mapping models, identifiability constraints affect PQL and INLA results
(Springer, 2018) Goicoa Mangado, Tomás; Adin Urtasun, Aritz; Ugarte Martínez, María Dolores; Hodges, James S.; Institute for Advanced Materials and Mathematics - INAMAT2
Disease mapping studies the distribution of relative risks or rates in space and time, and typically relies on generalized linear mixed models (GLMMs) including fixed effects and spatial, temporal, and spatio-temporal random effects. These GLMMs are typically not identifiable and constraints are required to achieve sensible results. However, automatic specification of constraints can sometimes lead to misleading results. In particular, the penalized quasi-likelihood fitting technique automatically centers the random effects even when this is not necessary. In the Bayesian approach, the recently-introduced integrated nested Laplace approximations computing technique can also produce wrong results if constraints are not wellspecified. In this paper the spatial, temporal, and spatiotemporal interaction random effects are reparameterized using the spectral decompositions of their precision matrices to establish the appropriate identifiability constraints. Breast cancer mortality data from Spain is used to illustrate the ideas.
Open Access
Flexible Bayesian P-splines for smoothing age-specific spatio-temporal mortality patterns
(SAGE, 2019) Goicoa Mangado, Tomás; Adin Urtasun, Aritz; Etxeberria Andueza, Jaione; Militino, Ana F.; Ugarte Martínez, María Dolores; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2
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 xed relative scale and scale invariant two-dimensional penalties are examined. Model tting and inference are carried out using integrated nested Laplace approximations (INLA), 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.
Open Access
Two-level resolution of relative risk of dengue disease in a hyperendemic city of Colombia
(Public Library of Science, 2018) Adin Urtasun, Aritz; Martínez Bello, Daniel Adyro; López Quílez, Antonio; Ugarte Martínez, María Dolores; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas
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.