Antonio López-Quílez

Bayesian Markov switching models for the early detection of influenza epidemics

The early detection of outbreaks of diseases is one of the most challenging objectives of epidemiological surveillance systems. In this paper, a Markov switching model is introduced to determine the epidemic and non-epidemic periods from influenza surveillance data: the process of differenced incidence rates is modelled either with a first-order autoregressive process or with a Gaussian white-noise process depending on whether the system is in an epidemic or in a non-epidemic phase. The transition between phases of the disease is modelled as a Markovian process. Bayesian inference is carried out on the former model to detect influenza epidemics at the very moment of their onset. Moreover, the proposal provides the probability of being in an epidemic state at any given moment. In order to validate the methodology, a comparison of its performance with other alternatives has been made using influenza illness data obtained from the Sanitary Sentinel Network of the Comunitat Valenciana, one of the 17 autonomous regions in Spain.

Detecting clusters of disease with R

One of the main concerns of Public Health surveillance is the detection of clusters of disease, i. e., the presence of high incidence rates around a particular location, which usually means a higher risk of suffering from the disease under study (Aylin et al. 1999). Many methods have been proposed for cluster detection, ranging from visual inspection of disease maps to full Bayesian models analysed using MCMC. In this paper we describe the use and implementation, as a package for the R programming language, of several methods which have been widely used in the literature, such as Openshaw’s GAM, Stone’s test and others. Although some of the statistics involved in these methods have an asymptotical distribution, bootstrap will be used to estimate their actual sampling distributions.

Bovine paramphistomosis in Galicia (Spain): Prevalence, intensity, aetiology and geospatial distribution of the infection

The present study explored various basic aspects of the epidemiology of paramphistomosis in Galicia, the main cattle producing region in Spain. In total, 589 cows from different farms located across the region were selected at random in the slaughterhouse for examination of the rumens and reticula for the presence of Paramphistomidae flukes. Paramphistomes were found in 111 of 589 necropsied cows (18.8%; 95% CI: 15.7-21.9%), with higher prevalences of infection in beef cows than in dairy cows (29.2% vs 13.9%). Although the number of flukes per animal was generally low (median=266 flukes), some cows harboured large parasite burdens (up to 11,895 flukes), which may have harmful effects on their health or productivity. Cows with higher parasite burdens also excreted greater numbers of fluke eggs in their faeces, which suggests that heavily parasitized mature cows play an important role in the transmission of paramphistomosis. This role may be particularly important in Galicia, where the roe deer, which is the only wild ruminant in the study area, was found not to be a reservoir for the infection. The use of morpho-anatomical and molecular techniques applied to a large number of fluke specimens provided reliable confirmation that Calicophoron daubneyi is the only species of the family Paramphistomidae that parasitizes cattle in Galicia. The environmental data from the farms of origin of the necropsied cows were used in Bayesian geostatistical models to predict the probability of infection by C. daubneyi throughout the region. The results revealed the role of environmental risk factors in explaining the geographical heterogeneity in the probability of infection in beef and dairy cattle. These explanatory factors were used to construct predictive maps showing the areas with the highest predicted risk of infection as well as the uncertainty associated with the predictions.

Estimation and prediction of the spatial occurrence of fish species using Bayesian latent Gaussian models

A methodological approach for modelling the occurrence patterns of species for the purpose of fisheries management is proposed here. The presence/absence of the species is modelled with a hierarchical Bayesian spatial model using the geographical and environmental characteristics of each fishing location. Maps of predicted probabilities of presence are generated using Bayesian kriging. Bayesian inference on the parameters and prediction of presence/absence in new locations (Bayesian kriging) are made by considering the model as a latent Gaussian model, which allows the use of the integrated nested Laplace approximation (INLA) software (which has been seen to be quite a bit faster than the well-known MCMC methods). In particular, the spatial effect has been implemented with the stochastic partial differential equation (SPDE) approach. The methodology is evaluated on Mediterranean horse mackerel (Trachurus mediterraneus) in the Western Mediterranean. The analysis shows that environmental and geographical factors can play an important role in directing local distribution and variability in the occurrence of species. Although this approach is used to recognize the habitat of mackerel, it could also be for other different species and life stages in order to improve knowledge of fish populations and communities.

Exploring neighborhood influences on small-area variations in intimate partner violence risk: A Bayesian random-effects modeling approach

This paper uses spatial data of cases of intimate partner violence against women (IPVAW) to examine neighborhood-level influences on small-area variations in IPVAW risk in a police district of the city of Valencia (Spain). To analyze area variations in IPVAW risk and its association with neighborhood-level explanatory variables we use a Bayesian spatial random-effects modeling approach, as well as disease mapping methods to represent risk probabilities in each area. Analyses show that IPVAW cases are more likely in areas of high immigrant concentration, high public disorder and crime, and high physical disorder. Results also show a spatial component indicating remaining variability attributable to spatially structured random effects. Bayesian spatial modeling offers a new perspective to identify IPVAW high and low risk areas, and provides a new avenue for the design of better-informed prevention and intervention strategies.

The Spatial Epidemiology of Intimate Partner Violence: Do Neighborhoods Matter?

We examined whether neighborhood-level characteristics influence spatial variations in the risk of intimate partner violence (IPV). Geocoded data on IPV cases with associated protection orders (n = 1,623) in the city of Valencia, Spain (2011-2013), were used for the analyses. Neighborhood units were 552 census block groups. Drawing from social disorganization theory, we explored 3 types of contextual influences: concentrated disadvantage, concentration of immigrants, and residential instability. A Bayesian spatial random-effects modeling approach was used to analyze influences of neighborhood-level characteristics on small-area variations in IPV risk. Disease mapping methods were also used to visualize areas of excess IPV risk. Results indicated that IPV risk was higher in physically disordered and decaying neighborhoods and in neighborhoods with low educational and economic status levels, high levels of public disorder and crime, and high concentrations of immigrants. Results also revealed spatially structured remaining variability in IPV risk that was not explained by the covariates. In this study, neighborhood concentrated disadvantage and immigrant concentration emerged as significant ecological risk factors explaining IPV. Addressing neighborhood-level risk factors should be considered for better targeting of IPV prevention.

Antimicrobial Resistance in More than 100,000 Escherichia coli Isolates According to Culture Site and Patient Age, Gender, and Location

ABSTRACT Escherichia coli and the antimicrobial pressure exerted on this microorganism can be modulated by factors dependent on the host. In this paper, we describe the distribution of antimicrobial resistance to amikacin, tobramycin, ampicillin, amoxicillin clavulanate, cefuroxime, cefoxitin, cefotaxime, imipenem, ciprofloxacin, fosfomycin, nitrofurantoin, and trimetoprim-sulfametoxazole in more than 100,000 E. coli isolates according to culture site and patient age, gender, and location. Bayesian inference was planned in all statistical analysis, and Markov chain Monte Carlo simulation was employed to estimate the model parameters. Our findings show the existence of a marked difference in the susceptibility to several antimicrobial agents depending on from where E. coli was isolated, with higher levels of resistance in isolates from medical devices, the respiratory system, and the skin and soft tissues; a higher resistance percentage in men than in women; and the existence of a clear difference in antimicrobial resistance with an age influence that cannot be explained merely by means of an increase of resistance after exposure to antimicrobials. Both men and women show increases in resistance with age, but while women show constant levels of resistance or slight increases during childbearing age and greater increases in the premenopausal age, men show a marked increase in resistance in the pubertal age. In conclusion, an overwhelming amount of data reveals the great adaptation capacity of E. coli and its close interaction with the host. Sex, age, and the origin of infection are determining factors with the ability to modulate antimicrobial resistances.

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.

Geostatistical computing of acoustic maps in the presence of barriers

Acoustic maps are the main diagnostic tools used by authorities for addressing the growing problem of urban acoustic contamination. Geostatistics models phenomena with spatial variation, but restricted to homogeneous prediction regions. The presence of barriers such as buildings introduces discontinuities in prediction areas. In this paper we investigate how to incorporate information of a geographical nature into the process of geostatistical prediction. In addition, we study the use of a Cost-Based distance to quantify the correlation between locations.

Determinants of between‐hospital variations in outcomes for patients admitted with COPD exacerbations: findings from a nationwide clinical audit (AUDIPOC) in Spain

Previous studies have demonstrated significant variability in the processes of care and outcomes of chronic obstructive pulmonary disease (COPD) exacerbations. The AUDIPOC is a Spanish nationwide clinical audit that identified large between‐hospital variations in care and clinical outcomes. Here, we test the hypothesis that these variations can be attributed to either patient characteristics, hospital characteristics and/or the so‐called hospital‐clustering effect, which indicates that patients with similar characteristics may experience different processes of care and outcomes depending on the hospital to which they are admitted.