Diabetes mellitus mortality in Spanish cities: Trends and geographical inequalities.

AIM: To analyze the geographical pattern of diabetes mellitus (DM) mortality and its association with socioeconomic factors in 26 Spanish cities. METHODS: We conducted an ecological study of DM mortality trends with two cross-sectional cuts (1996-2001; 2002-2007) using census tract (CT) as the unit of analysis. Smoothed standardized mortality rates (sSMR) were calculated using Bayesian models, and a socioeconomic deprivation score was calculated for each CT. RESULTS: In total, 27,757 deaths by DM were recorded, with higher mortality rates observed in men and in the period 1996-2001. For men, a significant association between CT deprivation score and DM mortality was observed in 6 cities in the first study period and in 7 cities in the second period. The highest relative risk was observed in Pamplona (RR, 5.13; 95% credible interval (95%CI), 1.32-15.16). For women, a significant association between CT deprivation score and DM mortality was observed in 13 cities in the first period and 8 in the second. The strongest association was observed in San Sebastián (RR, 3.44; 95%CI, 1.25-7.36). DM mortality remained stable in the majority of cities, although a marked decrease was observed in some cities, including Madrid (RR, 0.67 and 0.64 for men and women, respectively). CONCLUSIONS: Our findings demonstrate clear inequalities in DM mortality in Spain. These inequalities remained constant over time are were more marked in women. Detection of high-risk areas is crucial for the implementation of specific interventions.

A unifying modeling framework for highly multivariate disease mapping.

Multivariate disease mapping refers to the joint mapping of multiple diseases from regionally aggregated data and continues to be the subject of considerable attention for biostatisticians and spatial epidemiologists. The key issue is to map multiple diseases accounting for any correlations among themselves. Recently, Martinez-Beneito (2013) provided a unifying framework for multivariate disease mapping. While attractive in that it colligates a variety of existing statistical models for mapping multiple diseases, this and other existing approaches are computationally burdensome and preclude the multivariate analysis of moderate to large numbers of diseases. Here, we propose an alternative reformulation that accrues substantial computational benefits enabling the joint mapping of tens of diseases. Furthermore, the approach subsumes almost all existing classes of multivariate disease mapping models and offers substantial insight into the properties of statistical disease mapping models.

Bayesian hierarchical Poisson models with a hidden Markov structure for the detection of influenza epidemic outbreaks.

Considerable effort has been devoted to the development of statistical algorithms for the automated monitoring of influenza surveillance data. In this article, we introduce a framework of models for the early detection of the onset of an influenza epidemic which is applicable to different kinds of surveillance data. In particular, the process of the observed cases is modelled via a Bayesian Hierarchical Poisson model in which the intensity parameter is a function of the incidence rate. The key point is to consider this incidence rate as a normal distribution in which both parameters (mean and variance) are modelled differently, depending on whether the system is in an epidemic or non-epidemic phase. To do so, we propose a hidden Markov model in which the transition between both phases is modelled as a function of the epidemic state of the previous week. Different options for modelling the rates are described, including the option of modelling the mean at each phase as autoregressive processes of order 0, 1 or 2. Bayesian inference is carried out to provide the probability of being in an epidemic state at any given moment. The methodology is applied to various influenza data sets. The results indicate that our methods outperform previous approaches in terms of sensitivity, specificity and timeliness.

Geographical spread of influenza incidence in Spain during the 2009 A(H1N1) pandemic wave and the two succeeding influenza seasons.

The aim of this study was to monitor the spatio-temporal spread of influenza incidence in Spain during the 2009 pandemic and the following two influenza seasons 2010-2011 and 2011-2012 using a Bayesian Poisson mixed regression model; and implement this model of geographical analysis in the Spanish Influenza Surveillance System to obtain maps of influenza incidence for every week. In the pandemic wave the maps showed influenza activity spreading from west to east. The 2010-2011 influenza epidemic wave plotted a north-west/south-east pattern of spread. During the 2011-2012 season the spread of influenza was geographically heterogeneous. The most important source of variability in the model is the temporal term. The model of spatio-temporal spread of influenza incidence is a supplementary tool of influenza surveillance in Spain.

Spatial moving average risk smoothing.

This paper introduces spatial moving average risk smoothing (SMARS) as a new way of carrying out disease mapping. This proposal applies the moving average ideas of time series theory to the spatial domain, making use of a spatial moving average process of unknown order to define dependence on the risk of a disease occurring. Correlation of the risks for different locations will be a function of m values (m being unknown), providing a rich class of correlation functions that may be reproduced by SMARS. Moreover, the distance (in terms of neighborhoods) that should be covered for two units to be found to make the correlation of their risks 0 is a quantity to be fitted by the model. This way, we reproduce patterns that range from spatially independent to long-range spatially dependent. We will also show a theoretical study of the correlation structure induced by SMARS, illustrating the wide variety of correlation functions that this proposal is able to reproduce. We will also present three applications of SMARS to both simulated and real datasets. These applications will show SMARS to be a competitive disease mapping model when compared with alternative proposals that have already appeared in the literature. Finally, the application of SMARS to the study of mortality for 21 causes of death in the Comunitat Valenciana will allow us to identify some qualitative differences in the patterns of those diseases.

Childhood cancer incidence and survival in Spain.

BACKGROUND: This article affords an overview of the patterns and time trends of childhood cancer incidence (1983-2002) and survival (1991-2002) in Spain. PATIENTS AND METHODS: A population-based study was conducted, including 5936 cases for incidence and 3257 for survival analyses. Differences in incidence were tested with the standardised incidence ratio. Trends were analysed for all tumours, and for all malignant, haematological, central nervous system (CNS) (all and only malignant) and other solid tumours. Incidence trends were analysed using Poisson and Bayesian joinpoint models. Observed, relative and age-adjusted survival rates were calculated, and trends were tested using the log-rank test. RESULTS: The incidence pattern in Spain was similar to that in Europe. Rates, both overall and for leukaemias, lymphomas, CNS, soft tissue and, remarkably, for sympathetic nervous system and bone tumours, were high. Upward incidence trends were present for all tumour groups. All groups, except solid tumours (excluding CNS), displayed a change-point centred around 1990-95, after which the trend stopped rising. Five-year survival increased significantly across the period for all groups, except for CNS tumours. Recent survival results were in line with Italy, the UK and the European average. CONCLUSIONS: To confirm these results, ongoing surveillance of incidence and survival trends, and studies targeting specific tumours are called for.

An autoregressive approach to spatio-temporal disease mapping.

Disease mapping has been a very active research field during recent years. Nevertheless, time trends in risks have been ignored in most of these studies, yet they can provide information with a very high epidemiological value. Lately, several spatio-temporal models have been proposed, either based on a parametric description of time trends, on independent risk estimates for every period, or on the definition of the joint covariance matrix for all the periods as a Kronecker product of matrices. The following paper offers an autoregressive approach to spatio-temporal disease mapping by fusing ideas from autoregressive time series in order to link information in time and by spatial modelling to link information in space. Our proposal can be easily implemented in Bayesian simulation software packages, for example WinBUGS. As a result, risk estimates are obtained for every region related to those in their neighbours and to those in the same region in adjacent periods.

Analysis of the renal transplant waiting list in the País Valencià (Spain).

In this paper we analyse the renal transplant waiting list of the País Valencià in Spain, using Queueing theory. The customers of this queue are patients with end-stage renal failure waiting for a kidney transplant. We set up a simplified model to represent the flow of the customers through the system, and perform Bayesian inference to estimate parameters in the model. Finally, we consider several scenarios by tuning the estimations achieved and computationally simulate the behaviour of the queue under each one. The results indicate that the system could reach equilibrium at some point in the future and the model forecasts a slow decrease in the size of the waiting list in the short and middle term.

[Point processes as a tool for analyzing possible sources of contamination]. / Procesos puntuales como herramienta para el análisis de posibles fuentes de contaminación.

Point pattern analysis pattern comprises a series of techniques that enables the distribution of a series of events occurring in the vicinity of a particular region of a map to be studied. In epidemiology, this problem arises when a potential source of environmental contamination, possibly leading to cases of a specific disease, is investigated.The present study provides a brief description of point pattern analysis. The approach is illustrated through determination of the environmental source and study of the areas of greatest risk of incidence of an outbreak of legionella pneumonia that occurred between the middle of September and beginning of October in the city of Alcoi in Alicante (Spain).Point pattern analysis was able to confirm the environmental source of the outbreak and identify the areas of the city at greatest risk. This provided the justification for an exhaustive inspection of the installations generating aerosols after which, to date, the epidemics ceased.

Procesos puntuales como herramienta para el análisis de posibles fuentes de contaminación / Point processes as a tool for analyzing possible sources of contamination

El análisis de un patrón puntual engloba una serie de técnicas que permiten estudiar la distribución de un conjunto de eventos ocurridos sobre una región del plano. Este problema surge en epidemiología cuando se investiga una potencial fuente de contaminación ambiental alrededor de la cual se sospecha que surgen casos de una determinada enfermedad. En el presente trabajo, se explica brevemente en qué consiste el análisis de un patrón puntual y se ilustra con una aplicación a la determinación del origen medioambiental y al estudio de las zonas de mayor riesgo de incidencia en un brote de neumonía por Legionella ocurrido entre mediados de septiembre y principios de octubre en la ciudad de Alcoi (Alicante). El estudio permitió confirmar el origen medioambiental del brote y señalar las zonas de la ciudad con mayor riesgo, convirtiéndose en el argumento básico para llevar a cabo una exhaustiva inspección de las instalaciones generadoras de aerosoles, tras la cual, hasta la fecha, cesaron los brotes epidémicos (AU)

Point pattern analysis pattern comprises a series of techniques that enables the distribution of a series of events occurring in the vicinity of a particular region of a map to be studied. In epidemiology, this problem arises when a potential source of environmental contamination, possibly leading to cases of a specific disease, is investigated. The present study provides a brief description of point pattern analysis. The approach is illustrated through determination of the environmental source and study of the areas of greatest risk of incidence of an outbreak of legionella pneumonia that occurred between the middle of September and beginning of October in the city of Alcoi in Alicante (Spain). Point pattern analysis was able to confirm the environmental source of the outbreak and identify the areas of the city at greatest risk. This provided the justification for an exhaustive inspection of the installations generating aerosols after which, to date, the epidemics ceased (AU)

Incorporación de la metodología geoestadística a la vigilancia de la gripe en una red centinela / Incorporation of geostatistical methodology for influenza surveillance in a sentinel network

Objetivo: Valorar la descripción geoestadística realizada de los datos de gripe recogidos a través de la Red Centinela Sanitaria de la Comunidad Valenciana (RCSCV) mediante la utilización del método kriging con la finalidad de evaluar la posibilidad de su incorporación a la vigilancia rutinaria Método: Se han utilizado los datos de vigilancia de gripe de la RCSCV en tres temporadas gripales (1997-1998, 1998-1999 y 1999-2000), construyéndose una matriz de datos de incidencia de gripe geocodificada. La distribución geográfica fue estudiada mediante la técnica geoestadística kriging, que permite estimar la incidencia de la enfermedad en cualquier punto del territorio, a partir de la incidencia observada en unos pocos puntos estratégicamente distribuidos. Se elaboraron mapas de curvas de isoincidencia de gripe para cada semana. La valoración de la técnica se realizó mediante validación cruzada. Resultados: En la mayoría de las semanas, los valores tanto de la desviación estándar (DE) reducida, como de la media reducida estuvieron cercanos a los valores considerados óptimos (0 o 1, respectivamente), y sólo en la última temporada se obtuvieron valores de la DE reducida alejados de los considerados como de buen ajuste en 12 de las 20 semanas. La estimación de tasas en todas las temporadas demostró una coherencia en su distribución espacial. También se observó coherencia en la evolución temporal. Conclusiones: En la mayoría de las situaciones los resultados pueden considerarse aceptables, no requiere recursos informáticos extraordinarios ni un empleo de tiempo excesivo, y necesita tan sólo una adaptación anual. Su facilidad de uso lo hace apto para su utilización como una técnica de rutina, pese a que puede mejorarse la precisión de las estimaciones, incrementando la complejidad del modelo (AU)

Objectives: To evaluate geostatistical description of influenza data from the Valencian Sentinel Network (VSN) in Spain using the kriging method and to assess the possibility of incorporating this method into routine surveillance. Methods: We use influenza surveillance data on three influenza seasons (1997-1998, 1998-1999 and 1999-2000) from the VSN to construct a geocodified data matrix of the incidence of this disease. The geographic distribution was studied using the kriging method, which enables estimation of the incidence in a few strategically distributed points. Influenza isoincidence maps for each week were plotted. Cross validation was used to evaluate the method. Results: In most of the weeks, the values of reduced standard deviation and reduced mean were close to the optimal values (0 and 1, respectively). Out of range reduced standard deviation values were found in 12 of 20 weeks in the last season only. The estimation of rates in all three seasons showed coherence in spatial distribution and temporal evolution. Conclusions: In most situations the results were acceptable. The method does not requiere extra computer resources or an excessive amount of time and requires only annual adaptation. Becauseit is easy to use, the technique is appropriate for routine use but the accuracy of estimations could be improved by increasing the complexity of the model (AU)

[Incorporation of geostatistical methodology for influenza surveillance in a sentinel network]. / Incorporación de la metodología geoestadística a la vigilancia de la gripe en una red centinela.

OBJECTIVES: To evaluate geostatistical description of influenza data from the Valencian Sentinel Network (VSN) in Spain using the kringing method and to assess the possibility of incorporating this method into routine surveillance. METHODS: We use influenza surveillance data on three influenza seasons (1997-1998, 1998-1999 and 1999-2000) from the VSN to construct a geocodified data matrix of the incidence of this disease. The geographic distribution was studied using the kringing method, which enables estimation of the incidence in a few strategically distributed points. Influenza isoincidence maps for each week were plotted. Cross validation was used to evaluate the method. RESULTS: In most of the weeks, the values of reduced standard deviation and reduced mean were close to the optimal values (0 and 1, respectively). Out of range reduced standard deviation values were found in 12 of 20 weeks in the last season only. The estimation of rates in all three seasons showed coherence in spatial distribution and temporal evolution. CONCLUSIONS: In most situations the results were acceptable. The method does not require extra computer resources or an excessive amount of time and requires only annual adaptation. Because it is easy to use, the technique is appropriate for routine use but the accuracy of estimations could be improved by increasing the complexity of the model.