Performance of Outbreak Management Plans for Emerging Plant Diseases: The Case of Almond Leaf Scorch Caused by Xylella fastidiosa in Mainland Spain.

Outbreak response to quarantine pathogens and pests in the European Union (EU) is regulated by the EU Plant Health Law, but the performance of outbreak management plans in terms of their effectiveness and efficiency has been quantified only to a limited extent. As a case study, the disease dynamics of almond leaf scorch, caused by Xylella fastidiosa, in the affected area of Alicante, Spain, were approximated using an individual-based spatial epidemiological model. The emergence of this outbreak was dated based on phylogenetic studies, and official surveys were used to delimit the current extent of the disease. Different survey strategies and disease control measures were compared to determine their effectiveness and efficiency for outbreak management in relation to a baseline scenario without interventions. One-step and two-step survey approaches were compared with different confidence levels, buffer zone sizes, and eradication radii, including those set by the EU legislation for X. fastidiosa. The effect of disease control interventions was also considered by decreasing the transmission rate in the buffer zone. All outbreak management plans reduced the number of infected trees (effectiveness), but large differences were observed in the number of susceptible trees not eradicated (efficiency). The two-step survey approach, high confidence level, and the reduction in the transmission rate increased the efficiency. Only the outbreak management plans with the two-step survey approach removed infected trees completely, but they required greater survey efforts. Although control measures reduced disease spread, surveillance was the key factor in the effectiveness and efficiency of the outbreak management plans. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

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

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.

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

Circular leaf spot (CLS), caused by Plurivorosphaerella nawae, is a serious disease affecting persimmon (Diospyros kaki) that is characterized by necrotic lesions on leaves, defoliation, and fruit drop. Under Mediterranean conditions, P. nawae forms pseudothecia in the leaf litter in 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 previously 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 and placed in a wind tunnel, and the released ascospores of P. nawae 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 (ADDs) and ADDs 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.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

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

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.

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

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: jcarbonell.cipf@gmail.com or joaquin.dopazo@juntadeandalucia.es. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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

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.

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

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.

Relative risk estimation of dengue disease at small spatial scale.

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.

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

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.

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.

Bayesian hierarchical modelling of the geospatial distribution of fasciolosis in dairy cattle and the impact on production: Application to the main milk-producing region (Galicia) in Spain.

Fasciolosis caused by Fasciola hepatica is a common parasitic infection among cattle in many countries. Although infected adult cows rarely show overt clinical signs, milk production may be impaired. Thus, significant production losses may occur in dairy herds with a high prevalence of fasciolosis. In this study, Bayesian hierarchical modelling was used to estimate the geospatial distribution of dairy cattle fasciolosis and its impact on milk production. The study was conducted in Galicia, the main milk producing region in Spain and a geographically heterogeneous area. The aims were: 1) to model the geospatial distribution of fasciolosis in dairy herds in the study area, 2) to identify clusters of herds with a high prevalence of fasciolosis, and 3) to assess the effect of fasciolosis on milk yield and quality. A large number of dairy cattle farms (n = 4907), of which 1660 provided production records, were surveyed. Fasciola infection status was determined by applying the MM3-SERO ELISA test to bulk tank milk samples. A high probability of infection was predicted in several zones, particularly in the centre, northeast and southeast of Galicia. Conversely, the predicted probability was very low in some parts of the northwest of the region. Infections with high within-herd prevalence (> 25% lactating cows infected) predominated. High within-herd prevalence was associated with loss of milk production (-1.387 kg/cow/ day, on average). No association between Fasciola infection and either milk fat or protein content was observed. This study has generated the first maps of the spatial distribution of the probability of Fasciola infection in dairy cattle herds in Galicia. The maps presented here can be used for reference purposes, enabling the design of better targeted fasciolosis control programmes in the region. Use of Bayesian hierarchical statistical analysis enabled us to ascertain the uncertainty of the predictions and to account for the spatial autocorrelation in the data. It also enabled us to generate maps showing the residual spatial variation in milk production, a topic that may deserve more detailed study.

The Spatio-Temporal Distribution of Suicide-related Emergency Calls in a European City: Age and Gender Patterns, and Neighborhood Influences.

Objective: The aim of this study was to conduct a comprehensive spatio-temporal analysis of suicide-related emergency calls in the city of Valencia (Spain) over a six-year period. To this end we first examined age and gender patterns and, second, the influence of neighborhood characteristics on general and gender-specific spatio-temporal patterns of suicide-related emergency calls. Method: Geocoded data on suicide-related emergency calls between 2017 and 2022 (N = 10,030) were collected from the 112 emergency service in Valencia. Data were aggregated at the census block group level, used as a proxy for neighborhoods, and trimesters were considered as the temporal unit. Two set of analyses were performed: (1) demographic (age and gender) and temporal descriptive analyses and (2) general and gender-specific Bayesian spatio-temporal autoregressive models. Results: Descriptive analyses revealed a higher incidence of suicide-related emergency calls among females and an increase in calls among the 18-23 age group from 2020 onwards. The general spatio-temporal model showed higher levels of suicide-related emergency calls in neighborhoods characterized by lower education levels and population density, and higher residential mobility, aging population, and immigrant concentration. Relevant gender differences were also observed. A seasonal effect was noted, with a peak in calls during spring for females and summer for males. Conclusions: These findings highlight the need for comprehensive mental health targeted interventions and preventive strategies that account for gender-specific disparities, age-related vulnerabilities, and the specific characteristics of neighborhoods.

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

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.

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

Traditionally, intimate-partner violence has been considered a special type of crime that occurs behind closed doors, 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 (N = 26,624) and to intimate-partner violence against women (N = 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.

Statistical methods for the geographical analysis of rare diseases.

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).

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

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.

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

The plant-pathogenic bacterium Xylella fastidiosa 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. pauca. In Alicante, Spain, almond leaf scorch, caused by X. fastidiosa subsp. multiplex, was detected in 2017. The effects of climatic and spatial factors on the geographic distribution of X. fastidiosa in these two infested regions in Europe were studied. The presence/absence data of X. fastidiosa 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 X. fastidiosa subsp. fastidiosa. 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 via 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. multiplex. 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 (bio2) and the mean temperature of the wettest quarter (bio8) 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 X. fastidiosa in the study regions had arisen from a single focus or from several foci, which have been coalesced.

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

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.

Spatio-Temporal Analysis of Infectious Diseases.

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 [...].

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

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.