REPORT. First Workshop on SPATIO TEMPORAL DISEASE MAPPING

Transcription

1 REPORT First Workshop on SPATIO TEMPORAL DISEASE MAPPING Valencia, June 15th-16th, 2009 ORGANIZED BY:

2 ii Workshop Report The Workshop on SPATIO TEMPORAL DISEASE MAPPING has brought together experts in this field to review, discuss and explore directions of development of spatiotemporal methods for disease mapping. There has been invited and contributed sessions. Contributed talks, both theoretical and applied were welcome. The workshop was intended to appeal to researchers and practitioners in both Epidemiology and Statistics. The workshop was held in downtown Valencia in an informal environment (FUN- DACIÓ UNIVERSITAT-EMPRESA of the Universitat de València) to encourage discussion and promote further research in these fields. Six invited speakers presented talks and participated in the discussion session: Renato Assunção (Universidade Federal de Minas Gerais) Sudipto Banerjee (University of Minnesota) Annibale Biggeri (Università degli Studi di Firenze) María Durban (Universidad Carlos III de Madrid) Leonard Held (University of Zurich) Miguel-Ángel Martínez-Beneito (Centro Superior Investigación Salud Pública, CSISP) Two invited sessions were specially dedicated to the EUROHEIS2 project, with the participation of several partners: Facundo Muñoz (Universitat de València) Léa Fortunato (Small Area Health Statistics Unit, Imperial College London) Attila Juhasz (Regional Institute of Central Hungary of the National Public Health Medical Officer Service) Óscar Zurriaga (Centro Superior Investigación Salud Pública, CSISP) The workshop has been organised by the Universitat de València and endorsed by the i-math Consolider project, and by the EAHC (Executive Agency for Health and Consumers) through the EUROHEIS2 project.

3 Program iii Monday, June 15th :00-9:45 REGISTRATION AT ADEIT 9:45-10:00 WELCOME 10:00-11:30 EUROHEIS SESSION Chair: Antonio López-Quílez. Facundo Muñoz. Modelling and Implementation of spatio-temporal disease mapping. Léa Fortunato. Space-time patterns of disease risk using mixture models: new approach and sensitivity. Attila Juhasz. Changes of territorial inequalities among Hungarian population due to mortality of cancers of the Trachea, Bronchi and Lungs (ICD10: C33.C34) :30-12:00 Coffee break 12:00-13:30 INVITED SESSION Chair: María Durban. Renato Assunção. Visualizing marked spatial and origin-destination point patterns with dynamically linked windows. Miguel-Ángel Martínez Beneito. Spatio-temporal smoothing of risks based on spatial moving averages. 13:30-15:30 Lunch

4 iv Monday, June 15th :30-17:00 INVITED SESSION Chair: Carmen Armero. María Durban. P-spline mixed-models for spatio-temporal data. Leonard Held. Spatio-temporal disease mapping using INLA. 17:00-17:30 Orxata break 17:30-19:00 CONTRIBUTED SESSION Chair: David Conesa. Virgilio Gómez-Rubio. Fast Bayesian classification for disease mapping and the detection of disease clusters. Evans Gouno. Inference for Contamination Data. Birgit Schrödle. Evaluation of case reporting data from Switzerland.

5 v Tuesday, June 16th :00-11:30 INVITED SESSION Chair: Renato Assunção. Intervienen: Sudipto Banerjee. Smoothed ANOVA with spatial effects as a competitor to MCAR in multivariate spatial smoothing. Annibale Biggeri. Hierarchical Bayesian approaches to population health profiling. 11:30-12:00 Coffee break 12:00-13:00 EUROHEIS SESSION Chair: Antonio López-Quílez. Óscar Zurriaga. Uses of a digital spatio temporal mortality atlas. Usefulness of Spatio-Temporal Disease Mapping in public health decision making. Debate moderated by David Conesa (Universitat de València) 13:00-13:15 CLOSURE 13:15-15:00 Lunch

6 vi Sponsors Vicerectorat d Investigació i Tercer Cicle CONSOLIDER singular research project Ingenio Mathematica Grup d Estadística Espacial i Temporal en Epidemiologia i Medi Ambient Departament d Estadística i Investigació Operativa

7 Local organizing and scientific committees vii Carmen Armero Cervera Maria Jesús Bayarri García Paloma Botella-Rocamora David V. Conesa Guillén Antonio López Quílez Miguel A. Martínez-Beneito Universitat de València. Universitat de València. Universidad CEU-Cardenal Herrera. Universitat de València. Universitat de València. CSISP and Universitat de València. Web page

8 viii Index of abstracts Visualizing marked spatial and origin-destination point patterns with dynamically linked windows. R. Assunção and D. Lopes Smoothed ANOVA with spatial effects as a competitor to MCAR in multivariate spatial smoothing. S. Banerjee, Y. Zhang and J. S. Hodges Hierarchical Bayesian approaches to population health profiling. A. Biggeri, D. Catelan and C. Lagazio P-spline mixed-models for spatio-temporal data. M. Durban and D. J. Lee Space-time patterns of disease risk using mixture models: new approach and sensitivity. L. Fortunato, J. J. Abellán and S. Richardson Fast Bayesian classification for disease mapping and the detection of disease clusters. V. Gómez-Rubio Inference for Contamination Data. E. Gouno Spatio-temporal disease mapping using INLA L. Held and B. Schrödle Changes of Territorial Inequalities among Hungarian Population due to Mortality of Cancers of the Trachea, Bronchi and Lungs (ICD10: C33.C34) A. Juhász, C. Nagy and A. Páldy Spatio-temporal smoothing of risks based on spatial moving averages. M. A. Martínez-Beneito, P. Botella-Rocamora and A. López-Quílez Modelling and Implementation of spatio-temporal disease mapping. F. Muñoz-Viera and A. López-Quílez Evaluation of case reporting data from Switzerland. B. Schrödle and L. Held Uses of a digital spatio temporal mortality atlas. O. Zurriaga and M. A. Martínez-Beneito

9 Abstracts

10 Visualizing marked spatial and origin-destination point patterns with dynamically linked windows. Renato Assunção (Universidade Federal de Minas Gerais), joint work with Danilo Lopes. We present dynamic linked graphs for exploratory analysis of spatial marked point processes data. In addition to the usual marked point processes, we are also interested in a special type of marked point process. Namely, we analyse spatial bivariate point processes that are linked: each spatial event of a point process N1 has one or more corresponding events in another spatial point process N2 observed in the same geographical region. Pairs of origin-destination events are the main example of this type of data. We introduce MAPPEA, a graphical tool that displays the spatial point pattern and its marks in linked windows. Brushing over one view creates a linked view of the associated marks within the brush region. Two main applications are presently implemented: first, a dynamically changing cumulative distribution function of the univariate marks; and second, a dynamically changing map of the destination location conditional density distribution function given that the origin-event is within the brushed region. The methods are illustrated with data on car theft location and and the eventual car retrieval location, and on data of trees locations and their associated marks. 2

11 Smoothed ANOVA with spatial effects as a competitor to MCAR in multivariate spatial smoothing. Sudipto Banerjee (University of Minnesota), joint work with Yufen Zhang and James S. Hodges. Rapid developments in geographical information systems (GIS) and advanced spatial statistics continue to generate interest in analyzing complex spatial datasets. One area of activity is in creating smoothed disease maps to describe the geographic variation of disease and generate hypotheses for apparent differences in risk. With multiple diseases, a Multivariate Conditionally Autoregressive (MCAR) model is often used to smooth across space while accounting for associations between the diseases. The MCAR, however, imposes complex covariance structures that are difficult to interpret and estimate. This article develops a much simpler alternative approach building upon the techniques of smoothed ANOVA (SANOVA). Instead of simply shrinking effects without any structure, here we use SANOVA to smooth spatial random effects by taking advantage of the spatial structure. This paper extends SANOVA to cases in which one factor is a spatial lattice, which is smoothed using a CAR model, and a second factor is, for example, type of cancer. Datasets routinely lack enough information to identify the additional structure of MCAR. SANOVA offers a simpler and more intelligible structure than the MCAR while performing as well. We demonstrate our approach with simulation studies designed to compare SANOVA with different design matrices versus MCAR with different priors. Subsequently a cancer-surveillance dataset, describing incidence of 3 cancers in Minnesota s 87 counties, is analyzed using both approaches, showing the competitiveness of the SANOVA approach. 3

12 Hierarchical Bayesian approaches to population health profiling. Annibale Biggeri (Università degli Studi di Firenze), joint work with Dolores Catelan and Corrado Lagazio. Population Health profiling is an important phase in Environmental Epidemiology investigations. It consists in identifying altered rate of a disease among many diseases or, for a given disease, of an area within a region. We developed a Bayesian approach to model underlying risk pattern under the null and we used cross-validatory predictive distributions to generate model-based p-values (Ohlssen JRSSA 2007). Alternatively we specified a three level hierarchical Bayesian model and use the posterior classification probabilities as local FDR (Efron JASA 2001). We compare the results with other approaches in the literature (posterior probabilities under different models, simple FDR procedures for Poisson data). 4

13 P-spline mixed-models for spatio-temporal data. María Durban (Universidad Carlos III de Madrid), joint work with Dae-Jin Lee. In recent years, spatial and spatio-temporal modelling have become an important area of research in many fields (epidemiology, environmental studies, disease mapping,...). However, the methodology developed is constrained by the large amount of data available, and most models impose unrealistic constraints on the data in order to be fitted on a reasonable amount of time. We propose the use of Penalized splines (P-splines) in a mixed model framework for smoothing spatio-temporal data. Our approach allows the consideration of interaction terms which can be decomposed as a sum of smooth functions similarly as an ANOVA decomposition. These models are an attractive alternative due to their interpretability in terms of decompositions of smooth functions and basis which are identifiable. The properties of the basis allow the use of algorithms that can handle large amount of data. 5

14 Space-time patterns of disease risk using mixture models: new approach and sensitivity. Léa Fortunato (Imperial College), joint work with Juan José Abellán and Silvia Richardson. We have developed a Bayesian hierarchical model for the analysis of spatio-temporal data in the presence of interactions, in order to strengthen the interpretation of the spatial patterns of risk that are sustained over time and to pinpoint atypical areas showing evidence of unusual variability in the time pattern of the risk (Abellán et al., 2008). For the prior distribution of space-time interactions, a mixture model with two components is assumed. The first component reflects only residual noise, whereas the second one captures true departures from the space and time main effects. The posterior probabilities that each space-time interaction parameter comes from the second component are used to detect areas which do not follow the overall time trend. This approach has been compared with the space-time scan statistics (Kulldorff et al., 1997). As the assumed shape of the prior distribution of space-time interactions is likely to be influential, we have investigated alternative priors and used several Bayesian criteria for comparing these alternatives: DIC, posterior and mixed predictive p-values. Finally, we have applied these methods to analyse the spatio-temporal variations of the risk of bladder cancer in Utah ( ). Our models, of increasing complexity, were used to highlight unusual risk patterns. 6

15 Fast Bayesian classification for disease mapping and the detection of disease clusters. Virgilio Gómez-Rubio (Universidad de Castilla-La Mancha). In this work we propose a framework for the detection of clusters of disease based on Bayesian Hierarchical Models which extends the spatial scan statistic to a more general case. This extension is established my means of including possible clusters as a dummy variables in a regression model and performing a variable selection to identify the most relevant clusters. Instead of fitting several models using MCMC, which would be very time consuming, we have used approximate methods to compute the marginals of the coefficients of the cluster variables and other parameters of interest. With these approximate methods we can explore the space of possible clusters in a similar way as the spatial scan statistic. Cluster selection is performed with the DIC, so that not only fixed effects models can be compared. We discuss both the Binomial and Poisson cases, as well as mixed-effects models that can cope with overdispersed data. Models for dealing with zero-inflation are also considered. Finally, we show the advantages of this approach using several case studies. In particular, we consider detection of clusters of a single disease in space and space-time, joint clusters of two or more diseases and detection of clusters in the presence of zero-inflation. 7

16 Inference for Contamination Data. Evans Gouno (Université de Bretagne Sud). This work is motivated by an agricultural issue concerning sugarcane. Sugarcane can be infected with yellowing and stunting disease called sugarcane yellow leaf syndrome. The causal agent sugarcane yellow leaf virus (ScYLV) is transmitted by the aphid melanaphis sacchari. It is well-known that virus-free plants are quickly infected by proximity to other infected plants. We are interested in caracterizing the mechanisms which underlie the spread of the disease. We develop an approach based on survival analysis techniques by considering times to contamination and introducing a contamination factor depending on distance from infected area. A Weibull model is assumed, with a scale parameter depending on location and times to contamination. Maximum likelihood estimation are developed and a Bayesian approach is investigated to assess the contamination rate. We define and study the contamination risk factor. Results on real data are displayed. Simulation studies are conducted. 8

17 Spatio-temporal disease mapping using INLA. Leonard Held (University of Zurich), joint work with Birgit Schrödle. Integrated nested Laplace approximations (INLA) have been recently proposed for approximate Bayesian inference in latent Gaussian models (Rue, Martino and Chopin, 2009, JRSSB). The INLA approach is applicable to a wide range of commonly used statistical models, including models for spatial and spatio-temporal disease mapping. In this talk I will first review the INLA methodology and contrast it with more established inference approaches such as Markov chain Monte Carlo (MCMC). In the second part of the talk I will illustrate how parametric (Bernardinelli et al., Stats in Med, 1995) and nonparametric (Knorr-Held, Stats in Med, 2000) models for spatio-temporal disease mapping can be fitted using INLA. I will also discuss how the INLA approach can be used for model assessment and model comparison based on leave-one-out crossvalidation. The methodology will be applied to case reporting data on BVD (bovine viral diarrhoe) and Salmonellosis in cattle provided by the Swiss federal veterinary office. 9

18 Changes of Territorial Inequalities among Hungarian Population due to Mortality of Cancers of the Trachea, Bronchi and Lungs (ICD10: C33.C34) Attila Juhász (Regional Institute of Central Hungary of the National Public Health Medical Officer Service), joint work with Csilla Nagy and Anna Páldy. The mortality due to malignant diseases is extremely unfavourable in Hungary. The leading cause of cancer mortality in both sexes is that of the trachea and lungs. There is a considerable territorial inequality of mortality due to lung cancer inside the country, which was propped up by numerous epidemiological studies. The aim of our study was to reveal spatial distribution of SES adjusted premature mortality due to lung cancer (ICD-X: C33-C34) and to investigate the temporal changes of clusters between 1994 and 2007 years broken down into three years consecutive periods. The descriptive study was carried out by Rapid Inquiry Facility (RIF) and SaTScan software. Primary death place clusters were detected for females in and around the capital, Budapest during the whole investigation period. In case of men we did not find similar territorial accumulation of clusters unambiguously being restricted to an area as in case of women. However a fairly constant cluster of male cancer mortality can be identified in the Central-Eastern part of the country. From 2000 new clusters appeared in the North-Eastern, in the South- Eastern and Western part of the country. The results identified the population at risk in the examination period by using RIF. The risk analysis methodology can be applied in public health practice. 10

19 Spatio-temporal smoothing of risks based on spatial moving averages. Miguel Ángel Martínez-Beneito (CSISP and Universitat de València), joint work with Paloma Botella-Rocamora and Antonio López-Quílez. In recent years spatial modelling of disease occurrence has become very popular in epidemiological applications. Moreover, the use of Intrinsic Gaussian Markov Random Fields (IGMRF), with a heterogeneous effect for every region, has been the usual procedure to model the underlying risk variability in many of these studies. Nevertheless, the correlation structure in an IGMRF is completely determined by the geographical structure of the lattice under study, therefore it is not possible to adapt the dependence structure of this prior distribution to the geographical pattern of the disease under study. The goal of this study will be to propose an alternative class of spatial correlation structures different to IGMRF and extend its use for the spatio-temporal modelling of diseases. In the same way that IGMRF generalizes random walk processes to the spatial domain, we will resort to moving average ideas in time series to induce spatial dependence in our context. The model proposed will be formulated from a Bayesian perspective and Reversible Jump MCMC will be used to learn about the range (how many neighbouring regions away) of dependence of the spatial pattern studied. 11

20 Modelling and Implementation of spatio-temporal disease mapping. Facundo Muñoz-Viera (Universitat de València), joint work with Antonio López-Quílez. Among the specific objectives of the EUROHEIS 2 project is the inclusion of spatiotemporal methods for disease mapping in the Rapid Inquiry Facility (RIF). However, there is not a wide consensus on how to describe temporal and spatial evolution at the same time in a proper way. Although several spatio-temporal disease mapping techniques have been proposed recently, the implementation of these methods is not always easy or adequate for a quick response tool. In this talk we outline a general framework for spatio-temporal models, breaking them up into four stages: the probabilistic model for observations, the components of the linear predictor, the structures of the effects and the inference methodology. For each stage, the most commonly used alternatives in the literature are discussed, with special emphasis on the spatio-temporal interaction. We also review some of the most prominent proposals in the literature that have been used for spatio-temporal disease mapping. These models are classified according to the structure of the temporal trends that may arise, discussing their relative advantages and disadvantages. We conclude with a discussion on what we have identified as the most relevant aspects to be considered in the selection of a methodology for spatio-temporal disease mapping to be included in a RIF-like application. 12

21 Evaluation of case reporting data from Switzerland. Birgit Schrödle (University of Zurich), joint work with Leonard Held. Bayesian spatio-temporal models formulated in a hierarchical framework (Bernardinelli et al., 1995; Knorr-Held, 2000) are a useful tool for describing spatial and temporal patterns and identifying interactions between time and space within reported data of a disease. The presented application deals with case reporting data on several animal diseases provided by the Swiss federal veterinary office. While for some diseases active surveillance is carried out and reported cases are collected by surveillance programmes there are diseases which are monitored by passive surveillance only. Besides regional heterogeneity in prevalence of these diseases differences in disease occurrence may also be due to e.g. implications when a case is detected or the amount of knowledge on disease characteristics. Since the system of case registration for a region in Switzerland is highly connected with a liation to a canton importance of this factor also has to be investigated. Hence, several spatio-temporal models were fitted to the reporting data to detect unusual spatial and temporal trends. Conclusions on e.g. the sensitivity of the reporting data towards information campaigns, changes in disease awareness and veterinary political issues concerning a disease can be drawn from the obtained results. From an inferential point of view the usability of integrated nested Laplace approximations (Rue et al., 2009) as a tool for Bayesian inference in spatio-temporal models will be pointed out. 13

22 Uses of a digital spatio temporal mortality atlas. Óscar Zurriaga (Conselleria de Sanitat and Centro Superior de Investigación en Salud Pública), joint work with Miguel Ángel Martínez-Beneito Nowadays the use of statistical techniques for small areas estimates are very popular in epidemiology. The approach of Besag, York and Mollié (BYM) is the most accepted model in spatial studies but it ignores the temporal evolution of risk in the study region. Our interest of this work is to obtain a spatio-temporal view of mortality in the municipalities of the Comunitat Valenciana (a Mediterranean region of Spain) with a yearly temporal aggregation and to elaborate a digital atlas of mortality. We use an approach in which the risk is defined by temporal concatenation, as if it were an autoregressive time series of order 1, of spatial and heterogeneous random effects. The consideration of an appropriate dependence structure allows disaggregating in very small spatio-temporal units because information is shared in space and time and so this permits to obtain more reliable estimates. This model was implemented at municipal level in the Comunitat Valenciana for a wide range of mortality causes (selected cancers, circulatory and respiratory diseases, diabetes, AIDS and Alzheimer s disease) in the period , with spatial patterns previously studied through the BYM proposal. We obtained the Spatio-temporal Smoothed Standardized Mortality Ratio for each municipality and year of study. A digital mortality atlas (a web-based system with Geographic Information System -GIS- utilities) was produced. A collection of 460 maps (20 periods x 23 death causes) for each sex (920 totals) are shown in the digital atlas. This has allowed knowing the risk mortality evolution for the studied period. The maps from lung cancer in women and prostate cancer in men are remarkable. From them we can generate hypotheses about risk factors that may have been involved in their mortality distribution. By contrast, the maps of leukaemia in both sexes are homogeneous for the whole period, which does not allow for stating assumptions about underlying factors. A digital atlas like this is a novelty in the field of space-time studies and it makes available the latest information on risk distribution for each of the causes. 14