• abdominal aortic aneurysm (AAA)
• endovascular aneurysm repair
• mortality
• open aneurysm repair
• spatiotemporal analysis, geographic variation

• Humans
• Aortic Aneurysm, Abdominal/surgery
• Aortic Aneurysm, Abdominal/mortality
• Brazil/epidemiology
• Retrospective Studies
• Longitudinal Studies
• Male
• Health Services Accessibility
• Time Factors
• Female
• Healthcare Disparities
• Aged
• Risk Factors
• Vascular Surgical Procedures/mortality
• Vascular Surgical Procedures/adverse effects
• Treatment Outcome
• Aged, 80 and over
• Middle Aged
• Risk Assessment
• Spatio-Temporal Analysis

• Health equity
• Police violence
• Preterm birth

• Humans
• Police
• Premature Birth/epidemiology
• Premature Birth/ethnology
• Female
• United States/epidemiology
• Black or African American/statistics & numerical data
• Pregnancy
• Adult
• White People/statistics & numerical data
• Violence/statistics & numerical data
• Violence/ethnology
• Infant, Newborn
• Risk Factors

• R01 HD103684 NICHD NIH HHS
• R01HD103684 National Institute of Child Health and Human Development

• Opioid epidemic
• border region
• communities
• drug abuse
• overdose deaths

• Humans
• Mexico/epidemiology
• Bayes Theorem
• Drug Overdose/epidemiology
• Drug Overdose/prevention & control
• Behavioral Risk Factor Surveillance System
• Analgesics, Opioid

• Humans
• Brazil/epidemiology
• Leprosy/epidemiology
• Spatial Regression

Geographical (geospatial) clusters have been observed in inflammatory bowel disease (IBD) incidence and linked to environmental determinants of disease, but pediatric spatial patterns in North America are unknown. We hypothesized that we would identify geospatial clusters in the pediatric IBD (PIBD) population of British Columbia (BC), Canada and associate incidence with ethnicity and environmental exposures.

To identify PIBD clusters and model how spatial patterns are associated with population ethnicity and environmental exposures.

One thousand one hundred eighty-three patients were included from a BC Children’s Hospital clinical registry who met the criteria of diagnosis with IBD ≤ age 16.9 from 2001–2016 with a valid postal code on file. A spatial cluster detection routine was used to identify areas with similar incidence. An ecological analysis employed Poisson rate models of IBD, Crohn’s disease (CD), and ulcerative colitis (UC) cases as functions of areal population ethnicity, rurality, average family size and income, average population exposure to green space, air pollution, and vitamin-D weighted ultraviolet light from the Canadian Environmental Health Research Consortium, and pesticide applications.

Hot spots (high incidence) were identified in Metro Vancouver (IBD, CD, UC), southern Okanagan regions (IBD, CD), and Vancouver Island (CD). Cold spots (low incidence) were identified in Southeastern BC (IBD, CD, UC), Northern BC (IBD, CD), and on BC’s coast (UC). No high incidence hot spots were detected in the densest urban areas. Modeling results were represented as incidence rate ratios (IRR) with 95%CI. Novel risk factors for PIBD included fine particulate matter (PM_2.5) pollution (IRR = 1.294, CI = 1.113-1.507, P < 0.001) and agricultural application of petroleum oil to orchards and grapes (IRR = 1.135, CI = 1.007-1.270, P = 0.033). South Asian population (IRR = 1.020, CI = 1.011-1.028, P < 0.001) was a risk factor and Indigenous population (IRR = 0.956, CI = 0.941-0.971, P < 0.001), family size (IRR = 0.467, CI = 0.268-0.816, P = 0.007), and summer ultraviolet (IBD = 0.9993, CI = 0.9990–0.9996, P < 0.001) were protective factors as previously established. Novel risk factors for CD, as for PIBD, included: PM_2.5 air pollution (IRR = 1.230, CI = 1 .056-1.435, P = 0.008) and agricultural petroleum oil (IRR = 1.159, CI = 1.002-1.326, P = 0.038). Indigenous population (IRR = 0.923, CI = 0.895–0.951, P < 0.001), as previously established, was a protective factor. For UC, rural population (UC IRR = 0.990, CI = 0.983-0.996, P = 0.004) was a protective factor and South Asian population (IRR = 1.054, CI = 1.030–1.079, P < 0.001) a risk factor as previously established.

PIBD spatial clusters were identified and associated with known and novel environmental determinants. The identification of agricultural pesticides and PM_2.5 air pollution needs further study to validate these observations.

• Inflammatory bowel diseases
• Crohn disease
• Ulcerative colitis
• Pesticides
• Air pollution
• South Asian people

• Humans
• Child
• Adolescent
• Inflammatory Bowel Diseases/complications
• Colitis, Ulcerative/diagnosis
• Colitis, Ulcerative/epidemiology
• Colitis, Ulcerative/complications
• Crohn Disease/diagnosis
• Crohn Disease/epidemiology
• Crohn Disease/etiology
• Particulate Matter/adverse effects
• British Columbia/epidemiology
• Incidence

• Colombia
• cardiovascular diseases
• health disparities
• mortality
• neoplasms
• poverty
• respiratory tract diseases
• spatial analysis

• Cities
• Colombia/epidemiology
• Bayes Theorem
• Socioeconomic Factors
• Poverty
• Mortality

• dengue
• influential factors
• risk mapping
• spatial models
• urban environment

• Animals
• Bayes Theorem
• Advance Directives
• Algorithms
• Big Data
• Dengue/epidemiology

• Cesarean section
• Geographical information systems
• Spatial epidemiology
• Spatial interaction
• Spatiotemporal analysis

• big data
• electronic health records
• population health
• obesity

• Behavioral Risk Factor Surveillance System
• Census Tract
• Cross-Sectional Studies
• Humans
• Obesity/epidemiology
• Prevalence
• United States

• R00 LM012868 NLM NIH HHS

• disease map
• incidence
• kriging method
• oral cancer

• early childhood
• health inequalities
• respiratory diseases
• spatial filters

• Alberta/epidemiology
• Child
• Child, Preschool
• Cities
• Facilities and Services Utilization
• Healthcare Disparities
• Humans
• Respiratory Care Units/statistics & numerical data
• Retrospective Studies
• Socioeconomic Factors

• CIHR

To evaluate recent developments regarding the epidemiological situation of pseudorabies virus (PRV) infections in wild boar populations in Germany, nationwide serological monitoring was conducted between 2010 and 2015. During this period, a total of 108,748 sera from wild boars were tested for the presence of PRV-specific antibodies using commercial enzyme-linked immunosorbent assays. The overall PRV seroprevalence was estimated at 12.09% for Germany. A significant increase in seroprevalence was observed in recent years indicating both a further spatial spread and strong disease dynamics. For spatiotemporal analysis, data from 1985 to 2009 from previous studies were incorporated. The analysis revealed that PRV infections in wild boar were endemic in all German federal states; the affected area covers at least 48.5% of the German territory. There were marked differences in seroprevalence at district levels as well as in the relative risk (RR) of infection of wild boar throughout Germany. We identified several smaller clusters and one large region, where the RR was two to four times higher as compared to the remaining areas under investigation. Based on the present monitoring intensity and outcome, we provide recommendations with respect to future monitoring efforts concerning PRV infections in wild boar in Germany.

• Aujeszky’s disease
• Germany
• monitoring
• pseudorabies
• relative risk
• spatiotemporal analysis
• wild boar

The first case of human brucellosis in South Korea was reported in 2002, and cases of human infection continue to occur. Although an association between human and bovine brucellosis has been identified, the spatial relationship has not been studied in South Korea. Here, we analysed the spatial patterns of human and bovine brucellosis retrieved from the human and veterinary surveillance data, as well as the spatial correlation between human and bovine brucellosis and associated factors that contribute to its occurrence. The risk of human brucellosis was analysed using a Bayesian spatial model with potential risk factors. Our results show that, for both human and bovine brucellosis, hotspots were clustered in the southeast regions of Korea, whereas coldspots were clustered in the northwest regions of Korea. Our study suggests that the risk of human brucellosis increases in rural regions with the highest risk of bovine brucellosis. Collaborative strategies between human and veterinary health sectors (e.g, public health intervention and region-specific eradication programs for bovine brucellosis) would reduce the burden of brucellosis in South Korea.

• Plasmodium falciparum
• Plasmodium vivax
• Thai–Myanmar border
• spatial patterns
• temporal trends

• 106698/Z/14/Z Wellcome Trust

Recent studies have shown a high number of deaths from oral and oropharyngeal cancer worldwide, Brazil included. For this study, the deaths data (ICD-10, chapter II, categories C00 to C14) was obtained from Mortality Information System (SIM) and standardized by gender and population for each of the 554 Microregions of Brazil. The raw mortality rates were adopted as the standard and compared to the application of smoothing by the Bayesian model. In order to describe the geographical pattern of the occurrence of oral cancer, thematic maps were constructed, based on the distributions of mortality rates for Microregions and gender. Results: There were 7882 deaths registered due to oral and oropharyngeal cancer in Brazil, of which 6291 (79.81%) were male and 1591 (20.19%) female. The Empirical Bayesian Model presented greater scattering with mosaic appearance throughout the country, depicting high rates in Southeast and South regions interpolated with geographic voids of low rates in Midwest and North regions. For males, it was possible to identify expressive clusters in the Southeast and South regions. Conclusion: The Empirical Bayesian Model allowed an alternative interpretation of the oral and oropharynx cancer mortality mapping in Brazil.

• epidemiology
• health geography
• mortality
• oral cancer
• spatial

• Bayes Theorem
• Brazil/epidemiology
• Female
• Geographic Mapping
• Humans
• Male
• Mouth Neoplasms/epidemiology
• Mouth Neoplasms/mortality
• Oropharyngeal Neoplasms/epidemiology
• Oropharyngeal Neoplasms/mortality
• Sex Distribution

The aims of this study were to describe the temporal trend of OC from 2001 to 2016 and to analyze the space and space-time clusters of high mortality due to OC in Ecuador from 2011 to 2016. Methods. The present study is a mixed ecological study; the time trends were obtained using a Joinpoint regression model, space-time scan statistics was used to identify high-risk clusters, and Global Moran I index was calculated. Results. In Ecuador, between 2001 and 2016, OC caused a total of 1,025 deaths. Crude mortality rates significantly increased, with an APC (annual percentage change) of 2.7% (p=0.009). The age-standardized mortality rate did not significantly increase (APC: 1.73%; p=0.08). The most likely cluster was detected in 2015, included 20 cantons. The second cluster included 38 cantons, in the years 2014 to 2016. The Global Moran I index for the study period showed a negative spatial autocorrelation (−0.067; p=0.37). Conclusion. Mortality due to OC in Ecuador significantly increased over the 16-year study period, the young groups being the most affected. Ecuadorian provinces present high variability in types of OC and cancer rates.

Intestinal parasites infection is a major public health burden in low and middle-income countries. In Ghana, it is amongst the top five morbidities. In order to optimize scarce resources, reliable information on its geographical distribution is needed to guide periodic mass drug administration to populations of high risk. We analyzed district level morbidities of intestinal parasites between 2010 and 2014 using exploratory spatial analysis and geostatistics. We found a significantly positive Moran’s Index of spatial autocorrelation for each year, suggesting that adjoining districts have similar risk levels. Using local Moran’s Index, we found high-high clusters extending towards the Guinea and Sudan Savannah ecological zones, whereas low-low clusters extended within the semi-deciduous forest and transitional ecological zones. Variograms indicated that local and regional scale risk factors modulate the variation of intestinal parasites. Poisson kriging maps showed smoothed spatially varied distribution of intestinal parasites risk. These emphasize the need for a follow-up investigation into the exact determining factors modulating the observed patterns. The findings also underscored the potential of exploratory spatial analysis and geostatistics as tools for visualizing the spatial distribution of small area intestinal worms infections.

• Adolescent
• Adult
• Aged
• Aged, 80 and over
• Bayes Theorem
• Child
• Child, Preschool
• Cluster Analysis
• Developing Countries/statistics & numerical data
• Diarrhea/epidemiology
• Female
• Geography
• Ghana/epidemiology
• Humans
• Incidence
• Infant
• Infant, Newborn
• Male
• Middle Aged
• Rural Population/statistics & numerical data
• Space-Time Clustering
• Urban Population/statistics & numerical data
• Young Adult

Injuries are a leading cause of death and disability around the world. Injury incidence is often associated with socio-economic and physical environmental factors. The application of geospatial methods has been recognised as important to gain greater understanding of the complex nature of injury and the associated diverse range of geographically-diverse risk factors. Therefore, the aim of this paper is to provide an overview of geospatial methods applied in unintentional injury epidemiological studies.

Nine electronic databases were searched for papers published in 2000–2015, inclusive. Included were papers reporting unintentional injuries using geospatial methods for one or more categories of spatial epidemiological methods (mapping; clustering/cluster detection; and ecological analysis). Results describe the included injury cause categories, types of data and details relating to the applied geospatial methods.

From over 6,000 articles, 67 studies met all inclusion criteria. The major categories of injury data reported with geospatial methods were road traffic (n = 36), falls (n = 11), burns (n = 9), drowning (n = 4), and others (n = 7). Grouped by categories, mapping was the most frequently used method, with 62 (93%) studies applying this approach independently or in conjunction with other geospatial methods. Clustering/cluster detection methods were less common, applied in 27 (40%) studies. Three studies (4%) applied spatial regression methods (one study using a conditional autoregressive model and two studies using geographically weighted regression) to examine the relationship between injury incidence (drowning, road deaths) with aggregated data in relation to explanatory factors (socio-economic and environmental).

The number of studies using geospatial methods to investigate unintentional injuries has increased over recent years. While the majority of studies have focused on road traffic injuries, other injury cause categories, particularly falls and burns, have also demonstrated the application of these methods. Geospatial investigations of injury have largely been limited to mapping of data to visualise spatial structures. Use of more sophisticated approaches will help to understand a broader range of spatial risk factors, which remain under-explored when using traditional epidemiological approaches.

The online version of this article (doi:10.1186/s40621-016-0097-0) contains supplementary material, which is available to authorized users.

• Cluster detection
• Clustering
• Ecological analysis
• Geographical correlation
• Geographical epidemiology
• Mapping
• Smoothing
• Spatial analysis
• Spatial epidemiology

• Correlated heterogeneity (CH)
• Multiscale models
• Scaling effect
• Spatial mixture model
• Uncorrelated heterogeneity (UH)

• Epidemiology
• Humans
• Models, Statistical
• Risk Assessment

• R01 CA172805 NCI NIH HHS

Mosquitoes, Plasmodium parasites, and humans live in sympatry in some extra-Amazonian regions of Brazil. Recent migrations of people from Amazonia and other countries to extra-Amazonian regions have led to many malaria outbreaks. Lack of relevant expertise among health professionals in non-endemic areas can lead to a neglect of the disease, which can be dangerous given its high fatality rate. Therefore, understanding the spatial and temporal epidemiology of malaria is essential for developing strategies for disease control and elimination. This study aimed to characterize imported (IMP) and autochthonous/introduced (AU/IN) cases in the extra-Amazonian regions and identify risk areas and groups.

Epidemiological data collected between 2007 and 2014 were obtained from the Notifiable Diseases Information System of the Ministry of Health (SINAN) and from the Department of the Unified Health System (DATASUS). High malaria risk areas were determined using the Local Indicator of Spatial Association. IMP and AU/IN malaria incidence rates were corrected by Local Empirical Bayesian rates.

A total of 6092 malaria cases (IMP: 5416, 88.9 %; AU/IN: 676, 11.1 %) was recorded in the extra-Amazonian regions in 2007–2014. The highest numbers of IMP and AU/IN cases were registered in 2007 (n = 862) and 2010 (n = 149), respectively. IMP cases were more frequent than AU/IN cases in all states except for Espírito Santo. Piauí, Espírito Santo, and Paraná states had high incidences of AU/IN malaria. The majority of infections were by Plasmodium falciparum in northeast and southeast regions, while Plasmodium vivax was the predominant species in the south and mid-west showed cases of dual infection. AU/IN malaria cases were concentrated in the coastal region of Brazil, which contains the Atlantic Forest and hosts the Anopheles transmitters. Several malaria clusters were also associated with the Brazilian Pantanal biome and regions bordering the Amazonian biome.

Malaria is widespread outside the Amazonian region of Brazil, including in more urbanized and industrialized states. This fact is concerning because these highly populated areas retain favourable conditions for spreading of the parasites and vectors. Control measures for both IMP and AU/IN malaria are essential in these high-risk areas.

The online version of this article (doi:10.1186/s12936-015-0934-6) contains supplementary material, which is available to authorized users.

• Local Disease Rates
• Markov Chains
• Medicare
• Myocardial Infarction
• Spatial Analysis

• Aged
• Aged, 80 and over
• Bayes Theorem
• California/epidemiology
• Female
• Humans
• Male
• Markov Chains
• Medicare
• Monte Carlo Method
• Myocardial Infarction/epidemiology
• United States

• P01 AG019783 NIA NIH HHS
• P01AG19783 NIA NIH HHS

• dengue
• infestation index
• geographic information systems
• epidemiological surveillance

• Aedes
• Animals
• Bayes Theorem
• Brazil/epidemiology
• Dengue/epidemiology
• Dengue/transmission
• Dengue Virus/isolation & purification
• Disease Outbreaks/statistics & numerical data
• Geographic Information Systems
• Humans
• Incidence
• Insect Vectors
• Population Density
• Socioeconomic Factors
• Spatial Analysis

Visceral leishmaniasis (VL) is a vector-borne disease whose factors involved in transmission are poorly understood, especially in more urban and densely populated counties. In Brazil, the VL urbanization is a challenge for the control program. The goals were to identify the greater risk areas for human VL and the risk factors involved in transmission.

This is an ecological study on the relative risk of human VL. Spatial units of analysis were the coverage areas of the Basic Health Units (146 small-areas) of Belo Horizonte, Minas Gerais State, Brazil. Human VL cases, from 2007 to 2009 (n = 412), were obtained in the Brazilian Reportable Disease Information System. Bayesian approach was used to model the relative risk of VL including potential risk factors involved in transmission (canine infection, socioeconomic and environmental features) and to identify the small-areas of greater risk to human VL.

The relative risk of VL was shown to be correlated with income, education, and the number of infected dogs per inhabitants. The estimates of relative risk of VL were higher than 1.0 in 54% of the areas (79/146). The spatial modeling highlighted 14 areas with the highest relative risk of VL and 12 of them are concentrated in the northern region of the city.

The spatial analysis used in this study is useful for the identification of small-areas according to risk of human VL and presents operational applicability in control and surveillance program in an urban environment with an unequal spatial distribution of the disease. Thus the frequent monitoring of relative risk of human VL in small-areas is important to direct and prioritize the actions of the control program in urban environment, especially in big cities.

Visceral leishmaniasis (VL) is a vector-borne disease whose factors involved in transmission are poorly understood, especially in more urban and densely populated counties. In Brazil, the increasing occurrence of human VL cases in urban centers is a challenge for the control program. We aimed to identify the risk areas for VL and the risk factors involved in transmission in Belo Horizonte, a large urban area of the Brazil. At the same geographical space, we analyzed human VL cases (n = 412), canine infection and socioeconomic and environmental features. We identified a concentration of high-risk small-areas of human VL cases in the northern part of the city, marked by worse levels of education and income, and higher number of infected dogs per inhabitants. The spatial analysis used is useful for the identification of small-areas with a greater risk of VL and displays operational applicability in the control program in an urban environment with an unequal spatial distribution of the disease. Thus, the frequent monitoring of risk of human VL according to small-areas is important to direct and prioritize the actions of the control program in urban environment, especially in big cities.

Influenza is one of the most important leading causes of respiratory illness in the countries located in the tropical areas of South East Asia and Thailand. In this study the climate factors associated with influenza incidence in Chiang Mai Province, Northern Thailand, were investigated. Identification of factors responsible for influenza outbreaks and the mapping of potential risk areas in Chiang Mai are long overdue. This work examines the association between yearly climate patterns between 2001 and 2008 and influenza outbreaks in the Chiang Mai Province. The climatic factors included the amount of rainfall, percent of rainy days, relative humidity, maximum, minimum temperatures and temperature difference. The study develops a statistical analysis to quantitatively assess the relationship between climate and influenza outbreaks and then evaluate its suitability for predicting influenza outbreaks. A multiple linear regression technique was used to fit the statistical model. The Inverse Distance Weighted (IDW) interpolation and Geographic Information System (GIS) techniques were used in mapping the spatial diffusion of influenza risk zones. The results show that there is a significance correlation between influenza outbreaks and climate factors for the majority of the studied area. A statistical analysis was conducted to assess the validity of the model comparing model outputs and actual outbreaks.

The Community Health Information System (CHIS) online mapping system was first launched in 1998. Its overarching goal was to provide researchers, residents and organizations access to health related data reflecting the overall health and well-being of their communities within the Greater Houston area. In September 2009, initial planning and development began for the next generation of CHIS. The overarching goal for the new version remained to make health data easily accessible for a wide variety of research audiences. However, in the new version we specifically sought to make the CHIS truly interactive and give the user more control over data selection and reporting.

In July 2011, a beta version of the next-generation of the application was launched. This next-generation is also a web based interactive mapping tool comprised of two distinct portals: the Breast Health Portal and Project Safety Net. Both are accessed via a Google mapping interface. Geographic coverage for the portals is currently an 8 county region centered on Harris County, Texas. Data accessed by the application include Census 2000, Census 2010 (underway), cancer incidence from the Texas Cancer Registry (TX Dept. of State Health Services), death data from Texas Vital Statistics, clinic locations for free and low-cost health services, along with service lists, hours of operation, payment options and languages spoken, uninsured and poverty data.

The system features query on the fly technology, which means the data is not generated until the query is provided to the system. This allows users to interact in real-time with the databases and generate customized reports and maps. To the author's knowledge, the Breast Health Portal and Project Safety Net are the first local-scale interactive online mapping interfaces for public health data which allow users to control the data generated. For example, users may generate breast cancer incidence rates by Census tract, in real time, for women aged 40-64. Conversely, they could then generate the same rates for women aged 35-55. The queries are user controlled.

• Geographic Information Systems (GIS)
• dengue
• hotspot
• space-time cluster
• spatial statistics
• temporal analysis

• Adolescent
• Adult
• Child
• Child, Preschool
• Climate
• Dengue/epidemiology
• Disease Outbreaks
• Female
• Geographic Information Systems
• Humans
• Humidity
• Incidence
• Infant
• Infant, Newborn
• Male
• Middle Aged
• Rain
• Seasons
• Space-Time Clustering
• Temperature
• Thailand/epidemiology
• Young Adult

• Bayes Theorem
• Dengue/epidemiology
• Dengue/transmission
• Geographic Information Systems
• Geography
• Humans
• Incidence
• Population Density
• Population Surveillance
• Risk Factors
• Socioeconomic Factors
• Taiwan/epidemiology
• Temperature
• Urbanization