Publications by authors named "Antonio López-Quílez"

30 Publications

  • Page 1 of 1

The Spatial Overlap of Police Calls Reporting Street-Level and Behind-Closed-Doors Crime: A Bayesian Modeling Approach.

Int J Environ Res Public Health 2021 May 19;18(10). Epub 2021 May 19.

Department of Social Psychology, University of Valencia, 46010 Valencia, Spain.

Traditionally, intimate-partner violence has been considered a special type of crime that occurs , with different characteristics from street-level crime. The aim of this study is to analyze the spatial overlap of police calls reporting street-level and behind-closed-doors crime. We analyzed geocoded police calls in the 552 census-block groups of the city of Valencia, Spain, related to street-level crime ( = 26,624) and to intimate-partner violence against women ( = 11,673). A Bayesian joint model was run to analyze the spatial overlap. In addition, two Bayesian hierarchical models controlled for different neighborhood characteristics to analyze the relative risks. Results showed that 66.5% of the total between-area variation in risk of reporting street-level crime was captured by a shared spatial component, while for reporting IPVAW the shared component was 91.1%. The log relative risks showed a correlation of 0.53, with 73.6% of the census-block groups having either low or high values in both outcomes, and 26.4% of the areas with mismatched risks. Maps of the shared component and the relative risks are shown to detect spatial differences. These results suggest that although there are some spatial differences between police calls reporting street-level and behind-closed-doors crime, there is also a shared distribution that should be considered to inform better-targeted police interventions.
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http://dx.doi.org/10.3390/ijerph18105426DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8161302PMC
May 2021

Chronic high risk of intimate partner violence against women in disadvantaged neighborhoods: An eight-year space-time analysis.

Prev Med 2021 Jul 20;148:106550. Epub 2021 Apr 20.

Department of Social Psychology, University of Valencia, Av. Blasco Ibáñez, 21, 46010, Valencia, Spain.

We conducted a small-area ecological longitudinal study to analyze neighborhood contextual influences on the spatio-temporal variations in intimate partner violence against women (IPVAW) risk in a southern European city over an eight-year period. We used geocoded data of IPVAW cases with associated protection orders (n = 5867) in the city of Valencia, Spain (2011-2018). The city's 552 census block groups were used as the neighborhood units. Neighborhood-level covariates were: income, education, immigrant concentration, residential instability, alcohol outlet density, and criminality. We used a Bayesian autoregressive approach to spatio-temporal disease mapping. Neighborhoods with low levels of income and education and high levels of residential mobility and criminality had higher relative risk of IPVAW. Spatial patterns of high risk of IPVAW persisted over time during the eight-year period analyzed. Areas of stable low risk and with increasing or decreasing risk were also identified. Our findings link neighborhood disadvantage to the existence and persistence over time of spatial inequalities in IPVAW risk, showing that high risk of IPVAW can become chronic in disadvantaged neighborhoods. Our analytic approach provides specific risk estimates at the small-area level that are informative for intervention purposes, and can be useful to assess the effectiveness of prevention efforts in reducing IPVAW.
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http://dx.doi.org/10.1016/j.ypmed.2021.106550DOI Listing
July 2021

Modelling Inoculum Availability of Plurivorosphaerella nawae in Persimmon Leaf Litter with Bayesian Beta Regression.

Phytopathology 2020 Nov 24. Epub 2020 Nov 24.

Instituto Valenciano de Investigaciones Agrarias, Centro de Protección Vegetal y Biotecnología, Apdo Oficial, Valencia, Valencia, Spain, 46113;

Circular leaf spot (CLS), caused by Plurivorosphaerella nawae, is a serious disease affecting persimmon (Diospyros kaki) that induces necrotic lesions on leaves, defoliation and fruit drop. Under Mediterranean conditions, P. nawae forms pseudothecia in the leaf litter during winter and ascospores are released in spring infecting susceptible leaves. Persimmon growers are advised to apply fungicides for CLS control during the period of inoculum availability, which was defined based on ascospore counts under the microscope. A model of inoculum availability of P. nawae was developed and evaluated as an alternative to ascospore counts. Leaf litter samples were collected weekly in L'Alcúdia (Spain) from 2010 to 2015. Leaves were soaked, placed in a wind tunnel, and the ascospores of P. nawae released were counted. Hierarchical Bayesian beta regression methods were used to model the dynamics of ascospore production in the leaf litter. The selected model included accumulated degree days (ADD) and ADD taking into account the vapor pressure deficit (ADDvpd) as fixed effects, and year as random effect. This model had a mean absolute error of 0.042 and a root mean square error of 0.062. The beta regression model was evaluated in four orchards from 2010 to 2015. Higher accuracy was obtained at the beginning and the end of the ascospore production period, which are the events of interest to schedule fungicide sprays for CLS control in Spain. This same modeling framework can be extended to other fungal plant pathogens whose inoculum dynamics are expressed as proportion data.
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http://dx.doi.org/10.1094/PHYTO-08-20-0359-RDOI Listing
November 2020

Spatial Bayesian Modeling Applied to the Surveys of in Alicante (Spain) and Apulia (Italy).

Front Plant Sci 2020 14;11:1204. Epub 2020 Aug 14.

Centre de Protecció Vegetai i Biotecnología, Institut Valencià d'Investigacions Agràries (IVIA), Moncada, Spain.

The plant-pathogenic bacterium was first reported in Europe in 2013, in the province of Lecce, Italy, where extensive areas were affected by the olive quick decline syndrome, caused by the subsp. . In Alicante, Spain, almond leaf scorch, caused by subsp. , was detected in 2017. The effects of climatic and spatial factors on the geographic distribution of in these two infested regions in Europe were studied. The presence/absence data of in the official surveys were analyzed using Bayesian hierarchical models through the integrated nested Laplace approximation (INLA) methodology. Climatic covariates were obtained from the WorldClim v.2 database. A categorical variable was also included according to Purcell's minimum winter temperature thresholds for the risk of occurrence of Pierce's disease of grapevine, caused by subsp. . In Alicante, data were presented aggregated on a 1 km grid (lattice data), where the spatial effect was included in the model through a conditional autoregressive structure. In Lecce, data were observed at continuous locations occurring within a defined spatial domain (geostatistical data). Therefore, the spatial effect was included the stochastic partial differential equation approach. In Alicante, the pathogen was detected in all four of Purcell's categories, illustrating the environmental plasticity of the subsp. . Here, none of the climatic covariates were retained in the selected model. Only two of Purcell's categories were represented in Lecce. The mean diurnal range () and the mean temperature of the wettest quarter () were retained in the selected model, with a negative relationship with the presence of the pathogen. However, this may be due to the heterogeneous sampling distribution having a confounding effect with the climatic covariates. In both regions, the spatial structure had a strong influence on the models, but not the climatic covariates. Therefore, pathogen distribution was largely defined by the spatial relationship between geographic locations. This substantial contribution of the spatial effect in the models might indicate that the current extent of in the study regions had arisen from a single focus or from several foci, which have been coalesced.
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http://dx.doi.org/10.3389/fpls.2020.01204DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7456931PMC
August 2020

Disadvantaged neighborhoods and the spatial overlap of substantiated and unsubstantiated child maltreatment referrals.

Child Abuse Negl 2020 06 19;104:104477. Epub 2020 Apr 19.

Department of Statistics and Operations Research, University of Valencia, C/Doctor Moliner, 50, 46100, Burjassot, Valencia, Spain. Electronic address:

Background: Considerable debate exists on whether the substantiation decision is a reliable measure for rates of maltreatment. Studies have shown that risks among children victims of maltreatment versus children investigated but unsubstantiated are similar.

Objective: This paper aims to respond to two research questions: (1) Do most child maltreatment referrals, substantiated and unsubstantiated, come from the same neighborhoods? (2) Do substantiated and unsubstantiated referrals share the same neighborhood risk factors?

Participants And Settings: We used geocoded data from substantiated (n = 1799) and unsubstantiated (n = 1638) child maltreatment referrals in Valencia, Spain (2004-2015). As the neighborhood proxy, we used 552 Census block groups. Neighborhood characteristics analyzed were: socioeconomic status, immigration concentration, residential instability, and public disorder and crime.

Methods: To study the geographical overlap of child maltreatment referrals, a Bayesian joint modeling approach was used. To analyze the influence of neighborhood-level characteristics on risk, we used a Bayesian random-effects modeling approach.

Results: For substantiated child maltreatment referrals, 90 % of the total between-area variation in risk is captured by the shared component, while for unsubstantiated child maltreatment referrals, the shared component was 88 %. The correlation between substantiated and unsubstantiated risks of child maltreatment referrals was .80. These risks were higher in neighborhoods with low levels of socioeconomic status, higher immigrant concentration, public disorder and crime.

Conclusions: Child maltreatment referrals, regardless of whether substantiated or unsubstantiated, overlap in the same disadvantaged neighborhoods. This suggests that in these neighborhoods, families are at a higher risk of being investigated by child protective services suggesting a potential reporting bias.
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http://dx.doi.org/10.1016/j.chiabu.2020.104477DOI Listing
June 2020

Joint Estimation of Relative Risk for Dengue and Zika Infections, Colombia, 2015-2016.

Emerg Infect Dis 2019 06;25(6):1118-1126

We jointly estimated relative risk for dengue and Zika virus disease (Zika) in Colombia, establishing the spatial association between them at the department and city levels for October 2015-December 2016. Cases of dengue and Zika were allocated to the 87 municipalities of 1 department and the 293 census sections of 1 city in Colombia. We fitted 8 hierarchical Bayesian Poisson joint models of relative risk for dengue and Zika, including area- and disease-specific random effects accounting for several spatial patterns of disease risk (clustered or uncorrelated heterogeneity) within and between both diseases. Most of the dengue and Zika high-risk municipalities varied in their risk distribution; those for Zika were in the northern part of the department and dengue in the southern to northeastern parts. At city level, spatially clustered patterns of dengue high-risk census sections indicated Zika high-risk areas. This information can be used to inform public health decision making.
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http://dx.doi.org/10.3201/eid2506.180392DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6537708PMC
June 2019

Spatio-Temporal Analysis of Infectious Diseases.

Int J Environ Res Public Health 2019 02 25;16(4). Epub 2019 Feb 25.

Department of Statistics and Operations Research, Faculty of Mathematics, University of Valencia, 46100 Valencia, Spain.

Epidemiological research on the pathogenesis, diagnosis, and treatment of infectious diseases is a broad field of study with renewed validity in the face of social changes and new threats [...].
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http://dx.doi.org/10.3390/ijerph16040669DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6406380PMC
February 2019

Child maltreatment and alcohol outlets in Spain: Does the country drinking culture matters?

Child Abuse Negl 2019 05 26;91:23-30. Epub 2019 Feb 26.

College of Social Work, The Ohio State University, Stillman Hall, 1947 College Rd., Columbus, OH, 42310, USA. Electronic address:

Background: Alcohol outlet density has been linked to rates of substantiated maltreatment both cross-sectionally and over time. Most of these studies have been conducted in Anglo-Saxon countries, especially in the U.S., but other countries, where alcohol outlets and alcohol consumption may have different social meanings, are clearly underrepresented in the literature.

Objective: The aim of this study was to analyze whether alcohol outlet density is associated with neighborhood-level child maltreatment risk in a South-European city.

Participants And Setting: A longitudinal study was conducted in the city of Valencia (Spain). As spatial units, we used 552 census block groups. Family units with child maltreatment protection measures from 2004 to 2015 were geocoded (n = 1799).

Methods: A Bayesian spatio-temporal autoregression model was conducted to model the outcome variable.

Results: Results indicated that, once controlled for other neighborhood-level characteristics, the influence of off-premise density and restaurant/cafe density were not relevant, while bar density showed a negative relationship with child maltreatment risk. Spatially lagged alcohol outlet variables were also not relevant in the model.

Conclusions: Our results suggest the importance of taking into account the cultural influences on the relationship between alcohol outlets and child maltreatment risk. Future cross-cultural research is needed for better understanding this relationship.
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http://dx.doi.org/10.1016/j.chiabu.2019.02.010DOI Listing
May 2019

Accounting for preferential sampling in species distribution models.

Ecol Evol 2019 Jan 26;9(1):653-663. Epub 2018 Dec 26.

Departament ďEstadística i Investigació Operativa Universitat de València Valencia Spain.

Species distribution models (SDMs) are now being widely used in ecology for management and conservation purposes across terrestrial, freshwater, and marine realms. The increasing interest in SDMs has drawn the attention of ecologists to spatial models and, in particular, to geostatistical models, which are used to associate observations of species occurrence or abundance with environmental covariates in a finite number of locations in order to predict where (and how much of) a species is likely to be present in unsampled locations. Standard geostatistical methodology assumes that the choice of sampling locations is independent of the values of the variable of interest. However, in natural environments, due to practical limitations related to time and financial constraints, this theoretical assumption is often violated. In fact, data commonly derive from opportunistic sampling (e.g., whale or bird watching), in which observers tend to look for a specific species in areas where they expect to find it. These are examples of what is referred to as , which can lead to biased predictions of the distribution of the species. The aim of this study is to discuss a SDM that addresses this problem and that it is more computationally efficient than existing MCMC methods. From a statistical point of view, we interpret the data as a marked point pattern, where the sampling locations form a point pattern and the measurements taken in those locations (i.e., species abundance or occurrence) are the associated marks. Inference and prediction of species distribution is performed using a Bayesian approach, and integrated nested Laplace approximation (INLA) methodology and software are used for model fitting to minimize the computational burden. We show that abundance is highly overestimated at low abundance locations when preferential sampling effects not accounted for, in both a simulated example and a practical application using fishery data. This highlights that ecologists should be aware of the potential bias resulting from preferential sampling and account for it in a model when a survey is based on non-randomized and/or non-systematic sampling.
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http://dx.doi.org/10.1002/ece3.4789DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6342115PMC
January 2019

The university campus environment as a protective factor for intimate partner violence against women: An exploratory study.

J Community Psychol 2018 09 12;46(7):903-916. Epub 2018 Apr 12.

University of Valencia.

Some neighborhood characteristics linked to social disorganization theory have been related to intimate partner violence against women (IPVAW). The study of other neighborhood-level factors that may influence IPVAW risk, however, has received less attention. The aim of this study is to analyze the influence of university campuses on IPVAW risk. To conduct the study, IPVAW cases from 2011 to 2013 in the city of Valencia, Spain, were geocoded (n = 1,623). Census block groups were used as the neighborhood analysis unit. Distance between each census block group and the nearest university campus was measured. A Bayesian spatial model adjusted for census block group-level characteristics was performed. Results showed that the distance from a university campus was associated with an approximate 7% increase in IPVAW risk per kilometer. These results suggest that university campuses integrated in the city are related to IPVAW risk. Further research is needed to explain the mechanisms involved.
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http://dx.doi.org/10.1002/jcop.21980DOI Listing
September 2018

Two-level resolution of relative risk of dengue disease in a hyperendemic city of Colombia.

PLoS One 2018 11;13(9):e0203382. Epub 2018 Sep 11.

Department of Statistics, Computer Science, and Mathematics, Public University of Navarre, Spain.

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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0203382PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6133285PMC
February 2019

Spatio-Temporal Modeling of Zika and Dengue Infections within Colombia.

Int J Environ Res Public Health 2018 06 30;15(7). Epub 2018 Jun 30.

Epidemiologic Monitoring Office, Secretary of Health of the Department of Santander, Cl. 45 11-52 Bucaramanga, Colombia.

The aim of this study is to estimate the parallel relative risk of Zika virus disease (ZVD) and dengue using spatio-temporal interaction effects models for one department and one city of Colombia during the 2015⁻2016 ZVD outbreak. We apply the integrated nested Laplace approximation (INLA) for parameter estimation, using the epidemiological week (EW) as a time measure. At the departmental level, the best model showed that the dengue or ZVD risk in one municipality was highly associated with risk in the same municipality during the preceding EWs, while at the city level, the final model selected established that the high risk of dengue or ZVD in one census sector was highly associated not only with its neighboring census sectors in the same EW, but also with its neighboring sectors in the preceding EW. The spatio-temporal models provided smoothed risk estimates, credible risk intervals, and estimation of the probability of high risk of dengue and ZVD by area and time period. We explore the intricacies of the modeling process and interpretation of the results, advocating for the use of spatio-temporal models of the relative risk of dengue and ZVD in order to generate highly valuable epidemiological information for public health decision making.
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http://dx.doi.org/10.3390/ijerph15071376DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6068969PMC
June 2018

Neighborhood characteristics and violence behind closed doors: The spatial overlap of child maltreatment and intimate partner violence.

PLoS One 2018 7;13(6):e0198684. Epub 2018 Jun 7.

Department of Social Psychology, University of Valencia, Valencia, Spain.

In this study, we analyze first whether there is a common spatial distribution of child maltreatment (CM) and intimate partner violence (IPV), and second, whether the risks of CM and IPV are influenced by the same neighborhood characteristics, and if these risks spatially overlap. To this end we used geocoded data of CM referrals (N = 588) and IPV incidents (N = 1450) in the city of Valencia (Spain). As neighborhood proxies, we used 552 census block groups. Neighborhood characteristics analyzed at the aggregated level (census block groups) were: Neighborhood concentrated disadvantage (neighborhood economic status, neighborhood education level, and policing activity), immigrant concentration, and residential instability. A Bayesian joint modeling approach was used to examine the spatial distribution of CM and IPV, and a Bayesian random-effects modeling approach was used to analyze the influence of neighborhood-level characteristics on small-area variations of CM and IPV risks. For CM, 98% of the total between-area variation in risk was captured by a shared spatial component, while for IPV the shared component was 77%. The risks of CM and IPV were higher in neighborhoods characterized by lower levels of economic status and education, and higher levels of policing activity, immigrant concentration, and residential instability. The correlation between the log relative risk of CM and IPV was .85. Most census block groups had either low or high risks in both outcomes (with only 10.5% of the areas with mismatched risks). These results show that certain neighborhood characteristics are associated with an increase in the risk of family violence, regardless of whether this violence is against children or against intimate partners. Identifying these high-risk areas can inform a more integrated community-level response to both types of family violence. Future research should consider a community-level approach to address both types of family violence, as opposed to individual-level intervention addressing each type of violence separately.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0198684PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5991672PMC
January 2019

Neighborhood characteristics and violence behind closed doors: The spatial overlap of child maltreatment and intimate partner violence.

PLoS One 2018 7;13(6):e0198684. Epub 2018 Jun 7.

Department of Social Psychology, University of Valencia, Valencia, Spain.

In this study, we analyze first whether there is a common spatial distribution of child maltreatment (CM) and intimate partner violence (IPV), and second, whether the risks of CM and IPV are influenced by the same neighborhood characteristics, and if these risks spatially overlap. To this end we used geocoded data of CM referrals (N = 588) and IPV incidents (N = 1450) in the city of Valencia (Spain). As neighborhood proxies, we used 552 census block groups. Neighborhood characteristics analyzed at the aggregated level (census block groups) were: Neighborhood concentrated disadvantage (neighborhood economic status, neighborhood education level, and policing activity), immigrant concentration, and residential instability. A Bayesian joint modeling approach was used to examine the spatial distribution of CM and IPV, and a Bayesian random-effects modeling approach was used to analyze the influence of neighborhood-level characteristics on small-area variations of CM and IPV risks. For CM, 98% of the total between-area variation in risk was captured by a shared spatial component, while for IPV the shared component was 77%. The risks of CM and IPV were higher in neighborhoods characterized by lower levels of economic status and education, and higher levels of policing activity, immigrant concentration, and residential instability. The correlation between the log relative risk of CM and IPV was .85. Most census block groups had either low or high risks in both outcomes (with only 10.5% of the areas with mismatched risks). These results show that certain neighborhood characteristics are associated with an increase in the risk of family violence, regardless of whether this violence is against children or against intimate partners. Identifying these high-risk areas can inform a more integrated community-level response to both types of family violence. Future research should consider a community-level approach to address both types of family violence, as opposed to individual-level intervention addressing each type of violence separately.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0198684PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5991672PMC
January 2019

Mapping child maltreatment risk: a 12-year spatio-temporal analysis of neighborhood influences.

Int J Health Geogr 2017 10 18;16(1):38. Epub 2017 Oct 18.

Department of Social Psychology, University of Valencia, Av. Blasco Ibáñez, 21, 46010, Valencia, Spain.

Background: 'Place' matters in understanding prevalence variations and inequalities in child maltreatment risk. However, most studies examining ecological variations in child maltreatment risk fail to take into account the implications of the spatial and temporal dimensions of neighborhoods. In this study, we conduct a high-resolution small-area study to analyze the influence of neighborhood characteristics on the spatio-temporal epidemiology of child maltreatment risk.

Methods: We conducted a 12-year (2004-2015) small-area Bayesian spatio-temporal epidemiological study with all families with child maltreatment protection measures in the city of Valencia, Spain. As neighborhood units, we used 552 census block groups. Cases were geocoded using the family address. Neighborhood-level characteristics analyzed included three indicators of neighborhood disadvantage-neighborhood economic status, neighborhood education level, and levels of policing activity-, immigrant concentration, and residential instability. Bayesian spatio-temporal modelling and disease mapping methods were used to provide area-specific risk estimations.

Results: Results from a spatio-temporal autoregressive model showed that neighborhoods with low levels of economic and educational status, with high levels of policing activity, and high immigrant concentration had higher levels of substantiated child maltreatment risk. Disease mapping methods were used to analyze areas of excess risk. Results showed chronic spatial patterns of high child maltreatment risk during the years analyzed, as well as stability over time in areas of low risk. Areas with increased or decreased child maltreatment risk over the years were also observed.

Conclusions: A spatio-temporal epidemiological approach to study the geographical patterns, trends over time, and the contextual determinants of child maltreatment risk can provide a useful method to inform policy and action. This method can offer a more accurate description of the problem, and help to inform more localized prevention and intervention strategies. This new approach can also contribute to an improved epidemiological surveillance system to detect ecological variations in risk, and to assess the effectiveness of the initiatives to reduce this risk.
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http://dx.doi.org/10.1186/s12942-017-0111-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5648468PMC
October 2017

Reference genome assessment from a population scale perspective: an accurate profile of variability and noise.

Bioinformatics 2017 Nov;33(22):3511-3517

Computational Genomics, Principe Felipe Research Centre, Valencia 46012.

Motivation: Current plant and animal genomic studies are often based on newly assembled genomes that have not been properly consolidated. In this scenario, misassembled regions can easily lead to false-positive findings. Despite quality control scores are included within genotyping protocols, they are usually employed to evaluate individual sample quality rather than reference sequence reliability. We propose a statistical model that combines quality control scores across samples in order to detect incongruent patterns at every genomic region. Our model is inherently robust since common artifact signals are expected to be shared between independent samples over misassembled regions of the genome.

Results: The reliability of our protocol has been extensively tested through different experiments and organisms with accurate results, improving state-of-the-art methods. Our analysis demonstrates synergistic relations between quality control scores and allelic variability estimators, that improve the detection of misassembled regions, and is able to find strong artifact signals even within the human reference assembly. Furthermore, we demonstrated how our model can be trained to properly rank the confidence of a set of candidate variants obtained from new independent samples.

Availability And Implementation: This tool is freely available at http://gitlab.com/carbonell/ces.

Contact: [email protected] or [email protected]

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btx482DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870781PMC
November 2017

Relative risk estimation of dengue disease at small spatial scale.

Int J Health Geogr 2017 08 15;16(1):31. Epub 2017 Aug 15.

Epidemiological Surveillance, Health Office of Department of Santander, Cl. 45 11-52, Bucaramanga, 680001, Colombia.

Background: Dengue is a high incidence arboviral disease in tropical countries around the world. Colombia is an endemic country due to the favourable environmental conditions for vector survival and spread. Dengue surveillance in Colombia is based in passive notification of cases, supporting monitoring, prediction, risk factor identification and intervention measures. Even though the surveillance network works adequately, disease mapping techniques currently developed and employed for many health problems are not widely applied. We select the Colombian city of Bucaramanga to apply Bayesian areal disease mapping models, testing the challenges and difficulties of the approach.

Methods: We estimated the relative risk of dengue disease by census section (a geographical unit composed approximately by 1-20 city blocks) for the period January 2008 to December 2015. We included the covariates normalized difference vegetation index (NDVI) and land surface temperature (LST), obtained by satellite images. We fitted Bayesian areal models at the complete period and annual aggregation time scales for 2008-2015, with fixed and space-varying coefficients for the covariates, using Markov Chain Monte Carlo simulations. In addition, we used Cohen's Kappa agreement measures to compare the risk from year to year, and from every year to the complete period aggregation.

Results: We found the NDVI providing more information than LST for estimating relative risk of dengue, although their effects were small. NDVI was directly associated to high relative risk of dengue. Risk maps of dengue were produced from the estimates obtained by the modeling process. The year to year risk agreement by census section was sligth to fair.

Conclusion: The study provides an example of implementation of relative risk estimation using Bayesian models for disease mapping at small spatial scale with covariates. We relate satellite data to dengue disease, using an areal data approach, which is not commonly found in the literature. The main difficulty of the study was to find quality data for generating expected values as input for the models. We remark the importance of creating population registry at small spatial scale, which is not only relevant for the risk estimation of dengue but also important to the surveillance of all notifiable diseases.
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http://dx.doi.org/10.1186/s12942-017-0104-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5558735PMC
August 2017

Relative risk estimation of dengue disease at small spatial scale.

Int J Health Geogr 2017 08 15;16(1):31. Epub 2017 Aug 15.

Epidemiological Surveillance, Health Office of Department of Santander, Cl. 45 11-52, Bucaramanga, 680001, Colombia.

Background: Dengue is a high incidence arboviral disease in tropical countries around the world. Colombia is an endemic country due to the favourable environmental conditions for vector survival and spread. Dengue surveillance in Colombia is based in passive notification of cases, supporting monitoring, prediction, risk factor identification and intervention measures. Even though the surveillance network works adequately, disease mapping techniques currently developed and employed for many health problems are not widely applied. We select the Colombian city of Bucaramanga to apply Bayesian areal disease mapping models, testing the challenges and difficulties of the approach.

Methods: We estimated the relative risk of dengue disease by census section (a geographical unit composed approximately by 1-20 city blocks) for the period January 2008 to December 2015. We included the covariates normalized difference vegetation index (NDVI) and land surface temperature (LST), obtained by satellite images. We fitted Bayesian areal models at the complete period and annual aggregation time scales for 2008-2015, with fixed and space-varying coefficients for the covariates, using Markov Chain Monte Carlo simulations. In addition, we used Cohen's Kappa agreement measures to compare the risk from year to year, and from every year to the complete period aggregation.

Results: We found the NDVI providing more information than LST for estimating relative risk of dengue, although their effects were small. NDVI was directly associated to high relative risk of dengue. Risk maps of dengue were produced from the estimates obtained by the modeling process. The year to year risk agreement by census section was sligth to fair.

Conclusion: The study provides an example of implementation of relative risk estimation using Bayesian models for disease mapping at small spatial scale with covariates. We relate satellite data to dengue disease, using an areal data approach, which is not commonly found in the literature. The main difficulty of the study was to find quality data for generating expected values as input for the models. We remark the importance of creating population registry at small spatial scale, which is not only relevant for the risk estimation of dengue but also important to the surveillance of all notifiable diseases.
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http://dx.doi.org/10.1186/s12942-017-0104-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5558735PMC
August 2017

Bayesian dynamic modeling of time series of dengue disease case counts.

PLoS Negl Trop Dis 2017 Jul 3;11(7):e0005696. Epub 2017 Jul 3.

Secretaría de Salud del Departamento de Santander, Bucaramanga, Colombia.

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.
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http://dx.doi.org/10.1371/journal.pntd.0005696DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5510904PMC
July 2017

Validation of a Google Street View-Based Neighborhood Disorder Observational Scale.

J Urban Health 2017 04;94(2):190-198

University of Valencia, Valencia, Spain.

Recently, there has been a growing interest in developing new tools to measure neighborhood features using the benefits of emerging technologies. This study aimed to assess the psychometric properties of a neighborhood disorder observational scale using Google Street View (GSV). Two groups of raters conducted virtual audits of neighborhood disorder on all census block groups (N = 92) in a district of the city of Valencia (Spain). Four different analyses were conducted to validate the instrument. First, inter-rater reliability was assessed through intraclass correlation coefficients, indicating moderated levels of agreement among raters. Second, confirmatory factor analyses were performed to test the latent structure of the scale. A bifactor solution was proposed, comprising a general factor (general neighborhood disorder) and two specific factors (physical disorder and physical decay). Third, the virtual audit scores were assessed with the physical audit scores, showing a positive relationship between both audit methods. In addition, correlations between the factor scores and socioeconomic and criminality indicators were assessed. Finally, we analyzed the spatial autocorrelation of the scale factors, and two fully Bayesian spatial regression models were run to study the influence of these factors on drug-related police interventions and interventions with young offenders. All these indicators showed an association with the general neighborhood disorder. Taking together, results suggest that the GSV-based neighborhood disorder scale is a reliable, concise, and valid instrument to assess neighborhood disorder using new technologies.
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http://dx.doi.org/10.1007/s11524-017-0134-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391333PMC
April 2017

Modelling the presence of disease under spatial misalignment using Bayesian latent Gaussian models.

Geospat Health 2016 Apr 18;11(1):415. Epub 2016 Apr 18.

Operational Research Centre, Miguel Hernández de Elche University, Elche.

Modelling patterns of the spatial incidence of diseases using local environmental factors has been a growing problem in the last few years. Geostatistical models have become popular lately because they allow estimating and predicting the underlying disease risk and relating it with possible risk factors. Our approach to these models is based on the fact that the presence/absence of a disease can be expressed with a hierarchical Bayesian spatial model that incorporates the information provided by the geographical and environmental characteristics of the region of interest. Nevertheless, our main interest here is to tackle the misalignment problem arising when information about possible covariates are partially (or totally) different than those of the observed locations and those in which we want to predict. As a result, we present two different models depending on the fact that there is uncertainty on the covariates or not. In both cases, Bayesian inference on the parameters and prediction of presence/absence in new locations are made by considering the model as a latent Gaussian model, which allows the use of the integrated nested Laplace approximation. In particular, the spatial effect is implemented with the stochastic partial differential equation approach. The methodology is evaluated on the presence of the Fasciola hepatica in Galicia, a North-West region of Spain.
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http://dx.doi.org/10.4081/gh.2016.415DOI Listing
April 2016

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

Am J Epidemiol 2015 Jul 15;182(1):58-66. Epub 2015 May 15.

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.
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http://dx.doi.org/10.1093/aje/kwv016DOI Listing
July 2015

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

Int J Environ Res Public Health 2014 Jan 9;11(1):866-82. Epub 2014 Jan 9.

Department of Social Psychology, University of Valencia, Valencia 46010, Spain.

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.
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http://dx.doi.org/10.3390/ijerph110100866DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3924479PMC
January 2014

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

Vet Parasitol 2013 Jan 11;191(3-4):252-63. Epub 2012 Sep 11.

Centro de Investigaciones Agrarias de Mabegondo-INGACAL, Xunta de Galicia, Carretera Betanzos-Mesón do Vento, km 7, 15318 Abegondo, A Coruña, Spain.

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.
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http://dx.doi.org/10.1016/j.vetpar.2012.09.006DOI Listing
January 2013

Antimicrobial resistance in more than 100,000 Escherichia coli isolates according to culture site and patient age, gender, and location.

Antimicrob Agents Chemother 2011 Mar 10;55(3):1222-8. Epub 2011 Jan 10.

Hospital La Fe, Avda. Campanar 21, 46009 Valencia, Spain.

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.
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http://dx.doi.org/10.1128/AAC.00765-10DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3067086PMC
March 2011

Statistical methods for the geographical analysis of rare diseases.

Adv Exp Med Biol 2010 ;686:151-71

Departamento de Matemáticas, Universidad de Castilla-La Mancha, Escuela de Ingenieros Industriales, Albacete, Spain.

In this chapter we provide a summary of different methods for the detection of disease clusters. First of all, we give a summary of methods for computing estimates of the relative risk. These estimates provide smoothed values of the relative risks that can account for its spatial variation. Some methods for assessing spatial autocorrelation and general clustering are also discussed to test for significant spatial variation of the risk. In order to find the actual location of the clusters, scan methods are introduced. The spatial scan statistic is discussed as well as its extension by means of Generalised Linear Models that allows for the inclusion of covariates and cluster effects. In this context, zero-inflated models are introduced to account for the high number of zeros that appear when studying rare diseases. Finally, two applications of these methods are shown using data of Systemic Lupus Erythematosus in Spain and brain cancer in Navarre (Spain).
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http://dx.doi.org/10.1007/978-90-481-9485-8_10DOI Listing
December 2010

[Clinical audit of patients admitted to hospital in Spain due to exacerbation of COPD (AUDIPOC study): method and organisation].

Arch Bronconeumol 2010 Jul 31;46(7):349-57. Epub 2010 May 31.

Neumología, Hospital 12 de Octubre, Madrid, España; Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), España.

Background: There is little information regarding the clinical management of hospital inpatients diagnosed with exacerbation of Chronic Obstructive Pulmonary Disease (COPD). AUDIPOC is a clinical audit dealing with the clinical management of COPD in Spain.

Objectives: To examine the adequacy and validity of the instruments used to measure the variables proposed by AUDIPOC Spain (Preliminary Study) and to verify the viability of AUDIPOC in a complex environment with hospitals of different sizes, resources, and organizational layout (Pilot Study).

Materials And Methods: The Preliminary Study took place in 4 hospitals and studied 213 cases. The Pilot Study took place in 30 hospitals of 6 Autonomous Communities (i.e. Regions) and studied 1203 cases.

Results: The results of both studies contributed to the improvement of the design, methods and organization of the AUDIPOC work. Some of the improvements include better training of those responsible at a hospital level, a new classification of hospitals, the incorporation of new variables and the creation of a Bureau for the Coordination and Management of the Project.

Conclusions: The AUDIPOC study is viable. It aims to recruit 10000 patients across 142 hospitals from all the Regions of Spain.
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http://dx.doi.org/10.1016/j.arbres.2010.04.004DOI Listing
July 2010

FluDetWeb: an interactive web-based system for the early detection of the onset of influenza epidemics.

BMC Med Inform Decis Mak 2009 Jul 29;9:36. Epub 2009 Jul 29.

Departament d'Estadística i Investigació Operativa, Universitat de València, 46100 Burjassot, Valencia, Spain.

Background: The early identification of influenza outbreaks has became a priority in public health practice. A large variety of statistical algorithms for the automated monitoring of influenza surveillance have been proposed, but most of them require not only a lot of computational effort but also operation of sometimes not-so-friendly software.

Results: In this paper, we introduce FluDetWeb, an implementation of a prospective influenza surveillance methodology based on a client-server architecture with a thin (web-based) client application design. Users can introduce and edit their own data consisting of a series of weekly influenza incidence rates. The system returns the probability of being in an epidemic phase (via e-mail if desired). When the probability is greater than 0.5, it also returns the probability of an increase in the incidence rate during the following week. The system also provides two complementary graphs. This system has been implemented using statistical free-software (R and WinBUGS), a web server environment for Java code (Tomcat) and a software module created by us (Rdp) responsible for managing internal tasks; the software package MySQL has been used to construct the database management system. The implementation is available on-line from: http://www.geeitema.org/meviepi/fludetweb/.

Conclusion: The ease of use of FluDetWeb and its on-line availability can make it a valuable tool for public health practitioners who want to obtain information about the probability that their system is in an epidemic phase. Moreover, the architecture described can also be useful for developers of systems based on computationally intensive methods.
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http://dx.doi.org/10.1186/1472-6947-9-36DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2732617PMC
July 2009

Bayesian Markov switching models for the early detection of influenza epidemics.

Stat Med 2008 Sep;27(22):4455-68

Area de Epidemiología, Conselleria de Sanitat, Generalitat Valenciana, C/Micer Mascó 31, 46010 Valencia, Spain.

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.
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http://dx.doi.org/10.1002/sim.3320DOI Listing
September 2008

Spatial analysis of the relationship between mortality from cardiovascular and cerebrovascular disease and drinking water hardness.

Environ Health Perspect 2004 Jun;112(9):1037-44

Departamento d Estadistica i Investigacio Operativa, Universitat de Valencia, Valencia, Spain.

Previously published scientific papers have reported a negative correlation between drinking water hardness and cardiovascular mortality. Some ecologic and case-control studies suggest the protective effect of calcium and magnesium concentration in drinking water. In this article we present an analysis of this protective relationship in 538 municipalities of Comunidad Valenciana (Spain) from 1991-1998. We used the Spanish version of the Rapid Inquiry Facility (RIF) developed under the European Environment and Health Information System (EUROHEIS) research project. The strategy of analysis used in our study conforms to the exploratory nature of the RIF that is used as a tool to obtain quick and flexible insight into epidemiologic surveillance problems. This article describes the use of the RIF to explore possible associations between disease indicators and environmental factors. We used exposure analysis to assess the effect of both protective factors--calcium and magnesium--on mortality from cerebrovascular (ICD-9 430-438) and ischemic heart (ICD-9 410-414) diseases. This study provides statistical evidence of the relationship between mortality from cardiovascular diseases and hardness of drinking water. This relationship is stronger in cerebrovascular disease than in ischemic heart disease, is more pronounced for women than for men, and is more apparent with magnesium than with calcium concentration levels. Nevertheless, the protective nature of these two factors is not clearly established. Our results suggest the possibility of protectiveness but cannot be claimed as conclusive. The weak effects of these covariates make it difficult to separate them from the influence of socioeconomic and environmental factors. We have also performed disease mapping of standardized mortality ratios to detect clusters of municipalities with high risk. Further standardization by levels of calcium and magnesium in drinking water shows changes in the maps when we remove the effect of these covariates.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1247198PMC
http://dx.doi.org/10.1289/ehp.6737DOI Listing
June 2004