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Simple Summary: Spatial epidemiology, integrating traditional epidemiology, geography, statistics, environmental science, and ecology, provides a comprehensive framework for analyzing the spatial dimensions of health and disease. This interdisciplinary approach enhances the development of effective public health strategies and interventions. However, its multifaceted nature can bring complexities in practical application. Using the case of spatial epidemiology in swine viral diseases (SVDs), we illustrate the objectives, methodologies, and essential considerations for the application of spatial epidemiology, which we hope to offer as a comprehensive reference for researchers in this field. Spatial epidemiology offers a comprehensive framework for analyzing the spatial distribution and transmission of diseases, leveraging advanced technical tools and software, including Geographic Information Systems (GISs), remote sensing technology, statistical and mathematical software, and spatial analysis tools. Despite its increasing application to swine viral diseases (SVDs), certain challenges arise from its interdisciplinary nature. To support novices, frontline veterinarians, and public health policymakers in navigating its complexities, we provide a comprehensive overview of the common applications of spatial epidemiology in SVD. These applications are classified into four categories based on their objectives: visualizing and elucidating spatiotemporal distribution patterns, identifying risk factors, risk mapping, and tracing the spatiotemporal evolution of pathogens. We further elucidate the technical methods, software, and considerations necessary to accomplish these objectives. Additionally, we address critical issues such as the ecological fallacy and hypothesis generation in geographic correlation analysis. Finally, we explore the future prospects of spatial epidemiology in SVD within the One Health framework, offering a valuable reference for researchers engaged in the spatial analysis of SVD and other epidemics. [ABSTRACT FROM AUTHOR]

This study investigates the socio-spatial distribution of food deserts in Santiago de Chile, aiming to understand how urban planning and socioeconomic factors influence access to nutritious food. Employing geospatial analysis techniques with data from OpenStreetMap and the 2017 Census, the research identifies areas within Santiago where access to healthy food is limited. The novelty of this study lies in its application of spatial autocorrelation methods, specifically Local Indicators of Spatial Association (LISA), to reveal clusters of nutritional inequality. The findings indicate significant concentrations of food deserts in both lower socioeconomic peripheral areas and, surprisingly, in some high-income central areas. These results suggest that both poverty and urban infrastructure, including car dependency, play critical roles in shaping access to healthy food. By highlighting over two million residents affected by food deserts, the study underscores the urgent need for integrated urban planning and public health strategies. This research contributes to the understanding of urban nutritional inequality and provides a replicable methodological framework for other cities. The implications extend beyond Santiago, offering insights into how urban design can be leveraged to improve public health outcomes through better access to nutritious food. [ABSTRACT FROM AUTHOR]

We conducted an integrative analysis to elucidate the spatial epidemiological patterns of the Vesicular Stomatitis New Jersey virus (VSNJV) during the 2014–15 epizootic cycle in the United States (US). Using georeferenced VSNJV genomics data, confirmed vesicular stomatitis (VS) disease cases from surveillance, and a suite of environmental factors, our study assessed environmental and phylogenetic similarity to compare VS cases reported in 2014 and 2015. Despite uncertainties from incomplete virus sampling and cross-scale spatial processes, patterns suggested multiple independent re-invasion events concurrent with potential viral overwintering between sequential seasons. Our findings pointed to a geographically defined southern virus pool at the US–Mexico interface as the source of VSNJV invasions and overwintering sites. Phylodynamic analysis demonstrated an increase in virus diversity before a rise in case numbers and a pronounced reduction in virus diversity during the winter season, indicative of a genetic bottleneck and a significant narrowing of virus variation between the summer outbreak seasons. Environment–vector interactions underscored the central role of meta-population dynamics in driving disease spread. These insights emphasize the necessity for location- and time-specific management practices, including rapid response, movement restrictions, vector control, and other targeted interventions. [ABSTRACT FROM AUTHOR]

Spatial epidemiology investigates the patterns and determinants of health outcomes over both space and time. Within this field, Bayesian spatiotemporal models have gained popularity due to their capacity to incorporate spatial and temporal dependencies, uncertainties, and intricate interactions. However, the complexity of modelling and computations associated with Bayesian spatiotemporal models vary across different diseases. Presently, there is a limited comprehensive overview of Bayesian spatiotemporal models and their applications in epidemiology. This article aims to address this gap through a thorough review. The review commences by delving into the historical development of Bayesian spatiotemporal models concerning disease mapping, prediction, and regression analysis. Subsequently, the article compares these models in terms of spatiotemporal data distribution, general spatiotemporal data models, environmental covariates, parameter estimation methods, and model fitting standards. Following this, essential preparatory processes are outlined, encompassing data acquisition, data preprocessing, and available statistical software. The article further categorizes and summarizes the application of Bayesian spatiotemporal models in spatial epidemiology. Lastly, a critical examination of the advantages and disadvantages of these models, along with considerations for their application, is provided. This comprehensive review aims to enhance comprehension of the dynamic spatiotemporal distribution and prediction of epidemics. By facilitating effective disease scrutiny, especially in the context of the global COVID-19 pandemic, the review holds significant academic merit and practical value. It also aims to contribute to the development of improved ecological and epidemiological prevention and control strategies. [ABSTRACT FROM AUTHOR]

Peste des petits ruminants (PPR) is a highly contagious viral disease of small ruminants that threatens livelihoods and food security in developing countries and, in some cases, wild ungulate species conservation. The Greater Serengeti-Mara Ecosystem (GSME) encompasses one of the major wildlife populations of PPR virus (PPRV)-susceptible species left on earth, although no clinical disease has been reported so far. This study aimed to gain further knowledge about PPRV circulation in the GSME by identifying which factors predict PPRV seropositivity in African buffalo (Syncerus caffer). Following an ecological niche modeling framework to map host-pathogen distribution, two models of PPRV exposure and buffalo habitat suitability were performed using serological data and buffalo censuses. Western Maasai Mara National Reserve and Western Serengeti National Park were identified as high-risk areas for PPRV exposure in buffalo. Variables related to wildlife-livestock interaction contributed to the higher risk of PPRV seropositivity in buffalo, providing supportive evidence that buffalo acquire the virus through contact with infected livestock. These findings can guide the design of cost-effective PPRV surveillance using buffalo as a sentinel species at the identified high-risk locations. As more intensive studies have been carried out in Eastern GSME, this study highlights the need for investigating PPRV dynamics in Western GSME. [ABSTRACT FROM AUTHOR]

Modeling the windborne transmission of aerosolized pathogens is challenging. We adapted an atmospheric dispersion model named the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to simulate the windborne dispersion of porcine reproductive and respiratory syndrome virus (PRRSv) between swine farms and incorporated the findings into an outbreak investigation. The risk was estimated semi-quantitatively based on the cumulative daily deposition of windborne particles and the distance to the closest emitting farm with an ongoing outbreak. Five years of data (2014:2018) were used to study the seasonal differences of the deposition thresholds of the airborne particles containing PRRSv and to evaluate the model in relation to risk prediction and barn air filtration. When the 14-day cumulative deposition was considered, in winter, above-threshold particle depositions would reach up to 30 km from emitting farms with 84% of them being within 10 km. Long-distance pathogen transmission was highest in winter and fall, lower in spring, and least in summer. The model successfully replicated the observed seasonality of PRRSv, where fall and winter posed a higher risk for outbreaks. Reaching the humidity and temperature thresholds tolerated by the virus in spring and summer reduced the survival and infectivity of aerosols beyond 10–20 km. Within the data limitations of voluntary participation, when wind was assumed to be the sole route of PRRSv transmission, the predictive performance of the model was fair with >0.64 AUC. Barn air filtration was associated with fewer outbreaks, particularly when exposed to high levels of viral particles. This study confirms the usefulness of the HYSPLIT model as a tool when determining seasonal effects and distances and informs the near real-time risk of windborne PRRSv transmission that can be useful in future outbreak investigations and for implementing timely control measures. [ABSTRACT FROM AUTHOR]

Inappropriate antimicrobial use in animals and humans has been associated with the emergence of antimicrobial resistance, which has become a global public health concern. Veterinarians' practice locations and their knowledge and opinions on antimicrobial resistance may influence their antimicrobial prescription practices, which could impact the emergence of antimicrobial-resistant bacteria. This study used a spatial modeling approach to identify areas where veterinarians are knowledgeable about factors that impact the selection of antimicrobial resistance. In addition, we sought to identify regions with higher- and lower-than-expected response rates to our survey to aid future antimicrobial stewardship efforts. A total of 83 veterinarians who treated dogs and/or cats across 34 different Illinois counties responded to our online survey. Most of the responders (90.9%) considered that insufficient doses or duration of antibiotic treatments contribute the most to the selection of antimicrobial resistance. A high proportion of veterinarians (78.7%) attended educational programs on antimicrobial use and resistance; however, only 46.2% were knowledgeable about the current antimicrobial resistance profiles of prevalent bacteria in their area. A mean knowledge score for each county was calculated based on the responses of veterinarians to the survey questions. Local Moran's I statistic was used to identify counties with high and low knowledge scores. A high knowledge score area in the northeast region and a low knowledge score area in the southeast of Illinois were identified. Using scan statistics with a Poisson model that accounted for the estimated number of veterinarians in a county, a higher-than-expected response rate area was identified in central-east Illinois and a lower-than-expected area in the northeast. This study showed the effectiveness of using geographic analysis and spatial statistics to identify locations where future antimicrobial stewardship programs should focus. [ABSTRACT FROM AUTHOR]

Spatial expansions of vampire bat-transmitted rabies (VBR) are increasing the risk of lethal infections in livestock and humans in Latin America. Identifying the drivers of these expansions could improve current approaches to surveillance and prevention. We aimed to identify if VBR spatial expansions are occurring in Colombia and test factors associated with these expansions. We analyzed 2336 VBR outbreaks in livestock reported to the National Animal Health Agency (Instituto Colombiano Agropecuario—ICA) affecting 297 municipalities from 2000–2019. The area affected by VBR changed through time and was correlated to the reported number of outbreaks each year. Consistent with spatial expansions, some municipalities reported VBR outbreaks for the first time each year and nearly half of the estimated infected area in 2010–2019 did not report outbreaks in the previous decade. However, the number of newly infected municipalities decreased between 2000–2019, suggesting decelerating spatial expansions. Municipalities infected later had lower cattle populations and were located further from the local reporting offices of the ICA. Reducing the VBR burden in Colombia requires improving vaccination coverage in both endemic and newly infected areas while improving surveillance capacity in increasingly remote areas with lower cattle populations where rabies is emerging. [ABSTRACT FROM AUTHOR]

Modeling the windborne transmission of aerosolized pathogens is challenging. We adapted an atmospheric dispersion model (ADM) to simulate the windborne dispersion of porcine reproductive and respiratory syndrome virus (PRRSv) between swine farms. This work focuses on determining ADM applicable parameter values for PRRSv through a literature and expert opinion-based approach. The parameters included epidemiological features of PRRSv, characteristics of the aerosolized particles, and survival of aerosolized virus in relation to key meteorological features. A case study was undertaken to perform a sensitivity analysis on key parameters. Farms experiencing ongoing PRRSv outbreaks were assigned as particle emitting sources. The wind data from the North American Mesoscale Forecast System was used to simulate dispersion. The risk was estimated semi-quantitatively based on the median daily deposition of particles and the distance to the closest emitting farm. Among the parameters tested, the ADM was most sensitive to the number of particles emitted, followed by the model runtime, and the release height was the least sensitive. Farms within 25 km from an emitting farm were at the highest risk; with 53.66% being within 10 km. An ADM-based risk estimation of windborne transmission of PRRSv may inform optimum time intervals for air sampling, plan preventive measures, and aid in ruling out the windborne dispersion in outbreak investigations. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is a severe ongoing health crisisworldwide. Studying the socio-economic impacts of COVID-19 can help policymakers develop successful pandemic management plans. This paper focuses on the spatial epidemiology of COVID-19 among different social classes in the Kermanshah metropolis, Iran. This cross-sectional study uses the data of people infected with COVID-19 in the Kermanshah metropolis in 2020, acquired from the official COVID-19 Registry of Kermanshah. The results show that 2013 people were infected with COVID-19 (male = 1164 and female = 849). The mean age of the patients was 45 ± 18.69. The Moran's I show that COVID-19 in different social classes was clustered across the neighbourhoods in the Kermanshah metropolis. The mean ages of men and women were 44.51 ± 18.62 and 45.69 ± 18.76, respectively. Importantly, COVID-19 was highly prevalent in the middle-class groups. Age group comparisons indicate that older people were the most infected in poorer neighbourhoods. In the middle-classtheage group of 0–14 years and in the rich neighbourhoods the age group of 15–64 years were the most exposed to the disease. The findings of this study suggest that older people and lower socioeconomic classes should be prioritised while developing and implementing preventative programs for COVID-19 and similar pandemics. [ABSTRACT FROM AUTHOR]

Tick-borne encephalitis (TBE) is a growing public health problem with increasing incidence and expanding risk areas. Improved prevention requires better understanding of the spatial distribution and ecological determinants of TBE transmission. However, a TBE risk map at sub-district level is still missing for Germany. We investigated the distribution and geo-spatial characteristics of 567 self-reported places of probable TBE infection (POI) from 359 cases notified in 2018–2020 in the study area of Bavaria and Baden-Wuerttemberg, compared to 41 confirmed TBE foci and 1701 random comparator places. We built an ecological niche model to interpolate TBE risk to the entire study area. POI were distributed heterogeneously at sub-district level, as predicted probabilities varied markedly across regions (range 0–93%). POI were spatially associated with abiotic, biotic, and anthropogenic geo-spatial characteristics, including summer precipitation, population density, and annual frost days. The model performed with 69% sensitivity and 63% specificity at an optimised probability threshold (0.28) and an area under the curve of 0.73. We observed high predictive probabilities in small-scale areas, consistent with the known circulation of the TBE virus in spatially restricted microfoci. Supported by further field work, our findings may help identify new TBE foci. Our fine-grained risk map could supplement targeted prevention in risk areas. [ABSTRACT FROM AUTHOR]

Human mobility drives the geographical diffusion of infectious diseases at different scales, but few studies focus on mobility itself. Using publicly available data from Spain, we define a Mobility Matrix that captures constant flows between provinces by using a distance-like measure of effective distance to build a network model with the 52 provinces and 135 relevant edges. Madrid, Valladolid and Araba/Álaba are the most relevant nodes in terms of degree and strength. The shortest routes (most likely path between two points) between all provinces are calculated. A total of 7 mobility communities were found with a modularity of 63%, and a relationship was established with a cumulative incidence of COVID-19 in 14 days (CI14) during the study period. In conclusion, mobility patterns in Spain are governed by a small number of high-flow connections that remain constant in time and seem unaffected by seasonality or restrictions. Most of the travels happen within communities that do not completely represent political borders, and a wave-like spreading pattern with occasional long-distance jumps (small-world properties) can be identified. This information can be incorporated into preparedness and response plans targeting locations that are at risk of contagion preventively, underscoring the importance of coordination between administrations when addressing health emergencies.

Background: Several environmental factors seem to be involved in childhood leukaemia incidence. Traffic exposure could increase the risk while urban green spaces (UGS) exposure could reduce it. However, there is no evidence how these two factors interact on this infant pathology., Objectives: to evaluate how residential proximity to UGS could be an environmental protective factor against traffic exposure on childhood leukaemia incidence., Methods: A population-based case control study was conducted across thirty Spanish regions during the period 2000-2018. It included 2526 incident cases and 15,156, individually matched by sex, year-of-birth, and place-of-residence. Using the geographical coordinates of the participants' home residences, a 500 m proxy for exposure to UGS was built. Annual average daily traffic (AADT) was estimated for all types of roads 100 m near the children's residence. Odds ratios (ORs) and 95% confidence intervals (95% CIs), UGS, traffic exposure, and their possible interactions were calculated for overall childhood leukaemia, and the acute lymphoblastic (ALL) and acute myeloblastic leukaemia (AML) subtypes, with adjustment for socio-demographic covariates., Results: We found an increment of childhood leukaemia incidence related to traffic exposure, for every 100 AADT increase the incidence raised 1.1% (95% CI: 0.58-1.61%). UGS exposure showed an incidence reduction for the highest exposure level, Q5 (OR = 0.63; 95% CI = 0.54-0.72). Regression models with both traffic exposure and UGS exposure variables showed similar results but the interaction was not significant., Conclusions: Despite their opposite effects on childhood leukaemia incidence individually, our results do not suggest a possible interaction between both exposures. This is the first study about the interaction of these two environmental factors; consequently, it is necessary to continue taking into account more individualized data and other possible environmental risk factors involved.

This research examines risk factors for sporadic cryptosporidiosis and Escherichia coli (E. coli) O157 infection in East Tennessee, using a case-control approach and spatial logistic regression models. The risk factors examined are animal density, land use, geology, surface water impairment, poverty rate and availability of private water supply. Proximity to karst geology, beef cow population density and a high percentage of both developed land and pasture land are positively associated with both diseases. The availability of private water supply is negatively associated with both diseases. Risk maps generated using the model coefficients show areas of elevated risk to identify the communities where background risk is highest, so that limited public health resources can be targeted to the risk factors and communities most at risk. These results can be used as the framework upon which to develop a comprehensive epidemiological study that focuses on risk factors important at the individual level. [ABSTRACT FROM AUTHOR]

Biosurveillance activities focus on acquiring and analyzing epidemiological and biological data to interpret unfolding events and predict outcomes in infectious disease outbreaks. We describe a mathematical modeling framework based on geographically aligned data sources and with appropriate flexibility that partitions the modeling of disease spread into two distinct but coupled levels. A top-level stochastic simulation is defined on a network with nodes representing user-configurable geospatial "patches". Intra-patch disease spread is treated with differential equations that assume uniform mixing within the patch. We use U.S. county-level aggregated data on animal populations and parameters from the literature to simulate epidemic spread of two strikingly different animal diseases agents: foot-and-mouth disease and highly pathogenic avian influenza. Results demonstrate the capability of this framework to leverage low-fidelity data while producing meaningful output to inform biosurveillance and disease control measures. For example, we show that the possible magnitude of an outbreak is sensitive to the starting location of the outbreak, highlighting the strong geographic dependence of livestock and poultry infectious disease epidemics and the usefulness of effective biosurveillance policy. The ability to compare different diseases and host populations across the geographic landscape is important for decision support applications and for assessing the impact of surveillance, detection, and mitigation protocols. [ABSTRACT FROM AUTHOR]

The spatial patterns of adverse pregnancy outcomes (APOs) are complex, vary by place, and remain not entirely clear. This study investigated spatiotemporal patterns of APOs in rural areas of Henan, China. We used data from 1,315,327 singleton pregnancies during 2013-2016 in rural areas of Henan, China, from the National Free Pre-pregnancy Checkup Program (NFPCP). A spatiotemporal analysis of APOs was conducted based on the time of conception and current address. Results of seasonality decomposed showed a slight decline in the incidence rate of APOs (12.93% to 11.27% in the compound trend) among the participants from 2013 to 2016 and also variation in annual periodicity (peaking in autumn at 12.66% and hitting bottom in spring at 11.16%). Spatial clusters of APOs were concentrated in an intersection band of northwestern to southeastern Henan Province (with a relative risk ratio ranging from 3.66 to 1.20), the northwestern and northern portion for temporal variation (having a trend in the cluster ranged from -6.25% to 83.93). This study provides an overall picture of APOs that presented downward trends over time, seasonal fluctuation, and clustered patterns across space and over time in Henan Province-the most populated province in China. The findings of this study warrant future studies to investigate underlying influential factors of spatial variation of APOs.

Green space or natural vegetation may reduce obesity risk by increasing opportunities for physical activity or reducing stress and exposure to other pollutants. Obesity prevalence in Mexico is ranked among the highest in the world. However, research on the association between green space and obesity in Mexico is lacking. We used data from the National Nutrition Survey in Mexico (2018-2019), a nationally representative sample of Mexican adults. The analytical sample included participants between 20-59 years of age ( n = 12,631). We assessed exposure to green space using a 30 m resolution Landsat satellite Normalized Difference Vegetation Index (NDVI) from 2018. Linear regression models examined associations between NDVI and body mass index (BMI), adjusting for confounders. The mean age of the study sample was 38 (SD 0.19) years. Participants living in areas with the highest green space exposure had the lowest education level (53.51%) and socioeconomic status (28.38%) and were located in central (33.01%), south (30.37%), and rural areas (21.05%). Higher residential exposure to green space was associated with a mean decrease in BMI of -1.1 kg/m 2 (95% CI: -1.59, -0.68). This is one of the first studies in Latin America to suggest a protective association between green space and obesity among Mexican adults.

(1) Background: Although spatial statistics are often used by cancer epidemiologists, there is not yet an established collection of methods to serve their needs. We aimed to develop an evidence-based cancer-oriented conceptual collection of methods for spatial analysis; (2) Methods: A triangulation of approaches was used; literature review, consensus meetings (expert panel), and testing the selected methods on "training" databases. The literature review was conducted in three databases. This approach guided the development of a collection of methods that was subsequently commented on by the expert panel and tested on "training data" of cancer cases obtained from the Cancer Registry of Crete based on three epidemiological scenarios: (a) low prevalence cancers, (b) high prevalence cancers, (c) cancer and risk factors; (3) Results: The final spatial epidemiology conceptual collection of methods covered: data preparation/testing randomness, data protection, mapping/visualizing, geographic correlation studies, clustering/surveillance, integration of cancer data with socio-economic, clinical and environmental factors. Some of the tests/techniques included in the conceptual collection of methods were: buffer and proximity analysis, exploratory spatial analysis and others. All suggested that statistical models were found to fit well (R2 = 0.72-0.96) in "training data"; Conclusions: The proposed conceptual collection of methods provides public health professionals with a useful methodological framework along with recommendations for assessing diverse research questions of global health.

(1) Background: This is the first population-based study in Greece, with the aim to measure the changing trends of lung cancer (LC) and the associated risk factors before and after the economic crisis. Among the main objectives were the identification of LC hot spots and high-risk areas; (2) Methods: The study was conducted in Crete, the biggest island in Greece. Data (5057 LC cases) were collected from the Cancer Registry of Crete (CRC). The age-standardized incidence and mortality rates (ASIR, ASMR/100,000/year) were estimated, while additional indexes were used, including the adjusted Charlson's comorbidity index (CCI%), the deprivation index (HPI-2), and the exposure to outdoor air pollution (OAP). The analysis was performed for two time periods (Period A: 1992-2008; Period B: 2009-2013); (3) Results: ASIR presented a significant increase during the economic crisis, while an even higher increase was observed in ASMR (Period A: ASMR = 30.5/100,000/year; Period B: ASMR = 43.8/100,000/year; p < 0.001). After 2009, a significant increase in the observed LC hot spots was identified in several sub-regions in Crete ( p = 0.04). The risk of LC mortality increased even more for smokers (RR = 5.7; 95%CI = 5.2-6.3) and those living in highly deprived geographical regions (RR = 5.4; 95%CI = 5.1-5.8) during the austerity period. The multiple effect of LC predictors resulted in adjusted RRs ranging from 0.7 to 5.7 within the island ( p < 0.05); (4) Conclusions: The increased LC burden after the onset of the economic crisis, along with a changing pattern of LC predictors stressed the urgent need of geographically oriented interventions and cancer control programs focusing on the most deprived or vulnerable population groups.

Maps have become the de facto primary mode of visualizing the COVID-19 pandemic, from identifying local disease and vaccination patterns to understanding global trends. In addition to their widespread utilization for public communication, there have been a variety of advances in spatial methods created for localized operational needs. While broader dissemination of this more granular work is not commonplace due to the protections under Health Insurance Portability and Accountability Act (HIPAA), its role has been foundational to pandemic response for health systems, hospitals, and government agencies. In contrast to the retrospective views provided by the aggregated geographies found in the public domain, or those often utilized for academic research, operational response requires near real-time mapping based on continuously flowing address level data. This paper describes the opportunities and challenges presented in emergent disease mapping using dynamic patient data in the response to COVID-19 for northeast Ohio for the period 2020 to 2022. More specifically it shows how a new clustering tool developed by geographers in the initial phases of the pandemic to handle operational mapping continues to evolve with shifting pandemic needs, including new variant surges, vaccine targeting, and most recently, testing data shortfalls. This paper also demonstrates how the geographic approach applied provides the framework needed for future pandemic preparedness.

The health of Indigenous Australians is far poorer than non-Indigenous Australians, including an excess burden of infectious diseases. The health effect of built environmental (BE) features on Indigenous communities receives little attention. This study's objective was to determine associations between BE features and infectious disease incidence rates in remote Indigenous communities in the Northern Territory (NT), Australia. Remote Indigenous communities (n = 110) were spatially joined to 93 Indigenous Locations (ILOC). Outcomes data were extracted (NT Notifiable Diseases System) and expressed as ILOC-specific incidence rates. Counts of buildings were extracted from community asset maps and grouped by function. Age-adjusted infectious disease rates were dichotomised, and bivariate binomial regression used to determine the relationships between BE variables and infectious disease. Infrastructure Shelter BE features were universally associated with significantly elevated disease outcomes (relative risk 1.67 to 2.03). Significant associations were observed for Services, Arena, Community, Childcare, Oval, and Sports and recreation BE features. BE groupings associated with disease outcomes were those with communal and/or social design intent or use. Comparable BE groupings without this intent or use did not associate with disease outcomes. While discouraging use of communal BE features during infectious disease outbreaks is a conceptually valid countermeasure, communal activities have additional health benefits themselves, and infectious disease transmission could instead be reduced through repairs to infrastructure, and more infrastructure. This is the first study to examine these associations simultaneously in more than a handful of remote Indigenous communities to illustrate community-level rather than aggregated population-level associations. [ABSTRACT FROM AUTHOR]

Background: Blue spaces have been a key part of human evolution, providing resources and helping economies develop. To date, no studies have been carried out to explore how they may be linked to paediatric oncological diseases., Objectives: To explore the possible relationship of residential proximity to natural and urban blue spaces on childhood leukaemia., Methods: A population-based case-control study was conducted in four regions of Spain across the period 2000-2018. A total of 936 incident cases and 5616 controls were included, individually matched by sex, year of birth and place of residence. An exposure proxy with four distances (250 m, 500 m, 750 m, and 1 km) to blue spaces was built using the geographical coordinates of the participants' home residences. Odds ratios (ORs) and 95% confidence intervals (95%CIs) for blue-space exposure were calculated for overall childhood leukaemia, and the acute lymphoblastic (ALL) and acute myeloblastic leukaemia (AML) subtypes, with adjustment for socio-demographic and environmental covariates., Results: A decrease in overall childhood leukaemia and ALL-subtype incidence was found as we came nearer to children's places of residence, showing, for the study as a whole, a reduced incidence at 250 m (odds ratio (OR) = 0.77; 95%CI = 0.60-0.97), 500 m (OR = 0.78; 95%CI = 0.65-0.93), 750 m (OR = 0.80; 95%CI = 0.69-0.93), and 1000 m (OR = 0.84; 95%CI = 0.72-0.97). AML model results showed an increasing incidence at closest to subjects' homes (OR at 250m = 1.06; 95%CI=0.63-1.71)., Conclusions: Our results suggest a possible association between lower childhood leukaemia incidence and blue-space proximity. This study is a first approach to blue spaces' possible effects on childhood leukaemia incidence; consequently, it is necessary to continue studying these spaces-while taking into account more individualised data and other possible environmental risk factors.

Geospatial vaccine uptake is a critical factor in designing strategies that maximize the population-level impact of a vaccination program. This study uses an innovative spatiotemporal model to assess the impact of vaccination distribution strategies based on disease geospatial attributes and population-level risk assessment. For proof of concept, we adapted a spatially explicit COVID-19 model to investigate a hypothetical geospatial targeting of COVID-19 vaccine rollout in Ohio, United States, at the early phase of COVID-19 pandemic. The population-level deterministic compartmental model, incorporating spatial-geographic components at the county level, was formulated using a set of differential equations stratifying the population according to vaccination status and disease epidemiological characteristics. Three different hypothetical scenarios focusing on geographical subpopulation targeting (areas with high versus low infection intensity) were investigated. Our results suggest that a vaccine program that distributes vaccines equally across the entire state effectively averts infections and hospitalizations (2954 and 165 cases, respectively). However, in a context with equitable vaccine allocation, the number of COVID-19 cases in high infection intensity areas will remain high; the cumulative number of cases remained >30,000 cases. A vaccine program that initially targets high infection intensity areas has the most significant impact in reducing new COVID-19 cases and infection-related hospitalizations (3756 and 213 infections, respectively). Our approach demonstrates the importance of factoring geospatial attributes to the design and implementation of vaccination programs in a context with limited resources during the early stage of the vaccine rollout. [ABSTRACT FROM AUTHOR]

Background: Little is known about the extent to which socioenvironmental characteristics may influence mental health outcomes in smaller population centres or differently among women and men. This study used a gender-based analysis approach to explore individual- and neighbourhood-level sex differences in mental health service use in a context of uniquely smaller urban and rural settlements. Methods: This cross-sectional analysis leveraged multiple person-based administrative health datasets linked with geospatial datasets among the population aged 1 and over in the province of New Brunswick, Canada. We used multinomial logistic regression to examine associations between neighbourhood characteristics with risk of service contacts for mood and anxiety disorders in 2015/2016, characterizing the areal measures among all residents (gender neutral) and by males and females separately (gender specific), and controlling for age group. Results: Among the province's 707,575 eligible residents, 10.7% (females: 14.0%; males: 7.3%) used mental health services in the year of observation. In models adjusted for gender-neutral neighbourhood characteristics, service contacts were significantly more likely among persons residing in the most materially deprived areas compared with the least (OR = 1.09 [95% CI: 1.05-1.12]); when stratified by individuals' sex, the risk pattern held for females (OR = 1.13 [95% CI: 1.09-1.17]) but not males (OR = 1.00 [95% CI: 0.96-1.05]). Residence in the most female-specific materially deprived neighbourhoods was independently associated with higher risk of mental health service use among individual females (OR = 1.08 [95% CI: 1.02-1.14]) but not among males (OR = 1.02 [95% CI: 0.95-1.10]). Conclusion: These findings emphasize that research needs to better integrate sex and gender in contextual measures aiming to inform community interventions and neighbourhood designs, notably in small urban and rural settings, to reduce socioeconomic inequalities in the burden of mental disorders.

Simple Summary: Since African swine fever (ASF) virus in wild boar populations can spill over to domestic pigs, it is crucial to understand the disease determinants in the wild compartment. However, the imperfect detection sensitivity of wild boar surveillance jeopardizes our ability to understand ASF spatial distribution. In this study, we used national surveillance data of ASF in wild boars collected in the Republic of Korea from 2019–2020 to model the spatial distribution of ASF-positive carcasses for two successive study periods associated with different surveillance intensity. The model allowed us to identify disease risk factors in the Republic of Korea, determine the spatial distribution of the risk of ASF, and estimate the sensitivity of surveillance. The outputs of this study are relevant to policy makers for developing and improving risk-based surveillance programs for ASF in wild boars. In September 2019, African swine fever (ASF) was reported in South Korea for the first time. Since then, more than 651 ASF cases in wild boars and 14 farm outbreaks have been notified in the country. Despite the efforts to eradicate ASF among wild boar populations, the number of reported ASF-positive wild boar carcasses have increased recently. The purpose of this study was to characterize the spatial distribution of ASF-positive wild boar carcasses to identify the risk factors associated with the presence and number of ASF-positive wild boar carcasses in the affected areas. Because surveillance efforts have substantially increased in early 2020, we divided the study into two periods (2 October 2019 to 19 January 2020, and 19 January to 28 April 2020) based on the number of reported cases and aggregated the number of reported ASF-positive carcasses into a regular grid of hexagons of 3-km diameter. To account for imperfect detection of positive carcasses, we adjusted spatial zero-inflated Poisson regression models to the number of ASF-positive wild boar carcasses per hexagon. During the first study period, proximity to North Korea was identified as the major risk factor for the presence of African swine fever virus. In addition, there were more positive carcasses reported in affected hexagons with high habitat suitability for wild boars, low heat load index (HLI), and high human density. During the second study period, proximity to an ASF-positive carcass reported during the first period was the only significant risk factor for the presence of ASF-positive carcasses. Additionally, low HLI and elevation were associated with an increased number of ASF-positive carcasses reported in the affected hexagons. Although the proportion of ASF-affected hexagons increased from 0.06 (95% credible interval (CrI): 0.05–0.07) to 0.09 (95% CrI: 0.08–0.10), the probability of reporting at least one positive carcass in ASF-affected hexagons increased from 0.49 (95% CrI: 0.41–0.57) to 0.73 (95% CrI: 0.66–0.81) between the two study periods. These results can be used to further advance risk-based surveillance strategies in the Republic of Korea. [ABSTRACT FROM AUTHOR]

Prevention quality indicators (PQIs) constitute a set of measures that can be combined with hospital inpatient data to identify the quality of care for ambulatory care sensitive conditions (ACSC). Geographical information system (GIS) web mapping and applications contribute to a better representation of PQI spatial distribution. Unlike many countries in the world, in Portugal, this type of application remains underdeveloped. The main objective of this work was to facilitate the assessment of geographical patterns and trends of health data in Portugal. Therefore, two innovative open source applications were developed. Leaflet Javascript Library, PostGIS, and GeoServer were used to create a web map application prototype. Python language was used to develop the GIS application. The geospatial assessment of geographical patterns of health data in Portugal can be obtained through a GIS application and a web map application. Both tools proposed allowed for an easy and intuitive assessment of geographical patterns and time trends of PQI values in Portugal, alongside other relevant health data, i.e., the location of health care facilities, which, in turn, showed some association between the location of facilities and quality of health care. However, in the future, more research is still required to map other relevant data, for more in-depth analyses. [ABSTRACT FROM AUTHOR]

In recent decades, the incidence of Lyme borreliosis (LB) in Europe seems to have increased, underpinning a growing public health concern. LB surveillance systems across the continent are heterogeneous, and the spatial and temporal patterns of LB reports have been little documented. In this study, we explored the spatio-temporal patterns of LB cases reported in France from 2016 to 2019, to describe high-risk clusters and generate hypotheses on their occurrence. The space–time K-function and the Kulldorf's scan statistic were implemented separately for each year to evaluate space–time interaction between reported cases and searching clusters. The results show that the main spatial clusters, of radius size up to 97 km, were reported in central and northeastern France each year. In 2017–2019, spatial clusters were also identified in more southern areas (near the Alps and the Mediterranean coast). Spatio-temporal clustering occurred between May and August, over one-month to three-month windows in 2016–2017 and in 2018–2019. A strong spatio-temporal interaction was identified in 2018 within 16 km and seven days, suggesting a potential local and intense pathogen transmission process. Ongoing improved surveillance and accounting for animal hosts, vectors, meteorological factors and human behaviors are keys to further elucidate LB spatio-temporal patterns. [ABSTRACT FROM AUTHOR]

Epidemiologists and health geographers routinely use small-area survey estimates as covariates to model areal and even individual health outcomes. American Community Survey (ACS) estimates are accompanied by standard errors (SEs), but it is not yet standard practice to use them for evaluating or modeling data reliability. ACS SEs vary systematically across regions, neighborhoods, socioeconomic characteristics, and variables. Failure to consider probable observational error may have substantial impact on the large bodies of literature relying on small-area estimates, including inferential biases and over-confidence in results. The issue is particularly salient for predictive models employed to prioritize communities for service provision or funding allocation. Leveraging the tenets of plausible reasoning and Bayes' theorem, we propose a conceptual framework and workflow for spatial data analysis with areal survey data, including visual diagnostics and model specifications. To illustrate, we follow Krieger et al.'s (2018) call to routinely use the Index of Concentration at the Extremes (ICE) to monitor spatial inequalities in health and mortality. We construct and examine SEs for the ICE, use visual diagnostics to evaluate our observational error model for the ICE, and then estimate an ICE-mortality gradient by incorporating the latter model into our model of sex-specific, midlife (ages 55-64), all-cause United States county mortality rates. We urge researchers to consider data quality as a criterion for variable selection prior to modeling, and to incorporate data reliability information into their models whenever possible.

Natural disasters can have significant consequences for population mental health. Using a digital spatial epidemiologic approach, this study documents emotional changes over space and time in the context of a large-scale disaster. Our aims were to (a) explore the spatial distribution of negative emotional expressions of Twitter users before, during, and after Superstorm Sandy in New York City (NYC) in 2012 and (b) examine potential correlations between socioeconomic status and infrastructural damage with negative emotional expressions across NYC census tracts over time. A total of 984,311 geo-referenced tweets with negative basic emotions (anger, disgust, fear, sadness, shame) were collected and assigned to the census tracts within NYC boroughs between 8 October and 18 November 2012. Global and local univariate and bivariate Moran's I statistics were used to analyze the data. We found local spatial clusters of all negative emotions over all disaster periods. Socioeconomic status and infrastructural damage were predominantly correlated with disgust, fear, and shame post-disaster. We identified spatial clusters of emotional reactions during and in the aftermath of a large-scale disaster that could help provide guidance about where immediate and long-term relief measures are needed the most, if transferred to similar events and on comparable data worldwide.

Background: Hospital-Acquired Infections (HAIs) represent the most frequent adverse event associated with healthcare delivery and result in prolonged hospital stays and deaths worldwide., Aim: To analyze the spatial patterns of HAI incidence from 2014 to 2017 in Portugal., Methods: Data from the Portuguese Discharge Hospital Register were used. We selected episodes of patients with no infection on admission and with any of the following HAI diagnoses: catheter-related bloodstream infections, intestinal infections by Clostridium difficile , nosocomial pneumonia, surgical site infections, and urinary tract infections. We calculated age-standardized hospitalization rates (ASHR) by place of patient residence. We used empirical Bayes estimators to smooth the ASHR. The Moran Index and Local Index of Spatial Autocorrelation (LISA) were calculated to identify spatial clusters., Results: A total of 318,218 HAIs were registered, with men accounting for 49.8% cases. The median length of stay (LOS) was 9.0 days, and 15.7% of patients died during the hospitalization. The peak of HAIs ( n = 81,690) occurred in 2015, representing 9.4% of the total hospital admissions. Substantial spatial inequalities were observed, with the center region presenting three times the ASHR of the north. A slight decrease in ASHR was observed after 2015. Pneumonia was the most frequent HAI in all age groups., Conclusion: The incidence of HAI is not randomly distributed in the space; clusters of high risk in the central region were seen over the entire study period. These findings may be useful to support healthcare policymakers and to promote a revision of infection control policies, providing insights for improved implementation.

Chronic disease burdens continue to rise in highly dense urban environments where clustering of type II diabetes mellitus, acute myocardial infarction, stroke, or any combination of these three conditions is occurring. Many individuals suffering from these conditions will require longer-term care and access to clinics which specialize in managing their illness. With Singapore as a case study, we utilized census data in an agent-modeling approach at an individual level to estimate prevalence in 2020 and found high-risk clusters with >14,000 type II diabetes mellitus cases and 2000-2500 estimated stroke cases. For comorbidities, 10% of those with type II diabetes mellitus had a past acute myocardial infarction episode, while 6% had a past stroke. The western region of Singapore had the highest number of high-risk individuals at 173,000 with at least one chronic condition, followed by the east at 169,000 and the north with the least at 137,000. Such estimates can assist in healthcare resource planning, which requires these spatial distributions for evidence-based policymaking and to investigate why such heterogeneities exist. The methodologies presented can be utilized within any urban setting where census data exists.

The outbreak of SARS-CoV-2 in Wuhan, China in late December 2019 became the harbinger of the COVID-19 pandemic. During the pandemic, geospatial techniques, such as modeling and mapping, have helped in disease pattern detection. Here we provide a synthesis of the techniques and associated findings in relation to COVID-19 and its geographic, environmental, and socio-demographic characteristics, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology for scoping reviews. We searched PubMed for relevant articles and discussed the results separately for three categories: disease mapping, exposure mapping, and spatial epidemiological modeling. The majority of studies were ecological in nature and primarily carried out in China, Brazil, and the USA. The most common spatial methods used were clustering, hotspot analysis, space-time scan statistic, and regression modeling. Researchers used a wide range of spatial and statistical software to apply spatial analysis for the purpose of disease mapping, exposure mapping, and epidemiological modeling. Factors limiting the use of these spatial techniques were the unavailability and bias of COVID-19 data-along with scarcity of fine-scaled demographic, environmental, and socio-economic data-which restrained most of the researchers from exploring causal relationships of potential influencing factors of COVID-19. Our review identified geospatial analysis in COVID-19 research and highlighted current trends and research gaps. Since most of the studies found centered on Asia and the Americas, there is a need for more comparable spatial studies using geographically fine-scaled data in other areas of the world.

Central to developing effective control measures for the COVID-19 pandemic is understanding the epidemiology of transmission in the community. Geospatial analysis of neighborhood-level data could provide insight into drivers of infection. In the current analysis of Harris County, Texas, we used custom interpolation tools in GIS to disaggregate COVID-19 incidence estimates from the zip code to census tract estimates-a better representation of neighborhood-level estimates. We assessed the associations between 29 neighborhood-level characteristics and COVID-19 incidence using a series of aspatial and spatial models. The variables that maintained significant and positive associations with COVID-19 incidence in our final aspatial model and later represented in a geographically weighted regression model were the percentage of the Black/African American population, percentage of the foreign-born population, area derivation index (ADI), percentage of households with no vehicle, and percentage of people over 65 years old inside each census tract. Conversely, we observed negative and significant association with the percentage employed in education. Notably, the spatial models indicated that the impact of ADI was homogeneous across the study area, but other risk factors varied by neighborhood. The current findings could enhance decision making by local public health officials in responding to the COVID-19 pandemic. By understanding factors that drive community transmission, we can better target disease control measures.

Acquired Immune Deficiency Syndrome (AIDS) has become one of the most severe public health issues and nowadays around 38 million people are living with the human immunodeficiency virus (HIV). Ensuring healthy lives and promoting well-being is one of 17 United Nations Sustainable Development Goals. Here, we used the Markov chain matrix and geospatial clustering to comprehensively quantify the trends of the AIDS epidemic at the provincial administrate level in the mainland of China from 2005 to 2017. The Geographically Weighted Regression (GWR) model was further adopted to explore four groups of potential influencing factors (i.e., economy, traffic and transportation, medical care, and education) of the AIDS incidence rate in 2017 and their spatially distributed patterns. Results showed that the AIDS prevalence in southeastern China had been dominant and become prevalent in the past decade. The AIDS intensity level had been increasing between 2008 and 2011 but been gradually decreasing afterward. The analysis of the Markov chain matrix indicated that the AIDS epidemic has been generally in control on the Chinese mainland. The economic development was closely related to the rate of AIDS incidence on the Chinese mainland. The GWR result further suggested that medical care and the education effects on AIDS incidence rate can vary with different regions, but significant conclusions cannot be directly demonstrated. Our findings contribute an analytical framework of understanding AIDS epidemic trends and spatial variability of potential underlying factors throughout a complex extent to customize scientific prevention.

This paper presents an empirically grounded call for a more nuanced engagement and situatedness with placial characteristics within a spatial epidemiology frame. By using qualitative data collected through interviews and observation to parameterise standard and spatial regression models, and through a critical interpretation of their results, we present initial inroads for a situated spatial epidemiology and an analytical framework for health/medical geographers to iteratively engage with data, modelling, and the context of both the subject and process of analysis. In this study, we explore the socioeconomic factors that influence homicide rates in the Brazilian state of Alagoas from a critical public health perspective. Informed by field observation and interviews with 24 youths in low-income neighbourhoods and prisons in Alagoas, we derive and critically reflect on three regression models to predict municipal homicide rates from 2016-2020. The model results indicate significant effects for the male population, persons without elementary school completion, households with reported income, divorced persons, households without piped water, and persons working outside their home municipality. These results are situated in the broader socioeconomic context, trajectories, and cycles of inequality in the study area and underscore the need for integrative and contextually engaged mixed method study design in spatial epidemiology.

Background: Spatial epidemiology is used to evaluate geographical variations and disparities in health outcomes; however, constructing geographic statistical models requires a labor-intensive process that limits the overall utility. We developed an open-source software for spatial epidemiological analysis and demonstrated its applicability and quality., Methods: Based on standardized geocode and observational health data, the Application of Epidemiological Geographic Information System (AEGIS) provides two spatial analysis methods: disease mapping and detecting clustered medical conditions and outcomes. The AEGIS assesses the geographical distribution of incidences and health outcomes in Korea and the United States, specifically incidence of cancers and their mortality rates, endemic malarial areas, and heart diseases (only the United States)., Results: The AEGIS-generated spatial distribution of incident cancer in Korea was consistent with previous reports. The incidence of liver cancer in women with the highest Moran's I (0.44; p < 0.001) was 17.4 (10.3-26.9). The malarial endemic cluster was identified in Paju-si, Korea ( p < 0.001). When the AEGIS was applied to the database of the United States, a heart disease cluster was appropriately identified ( p < 0.001)., Conclusions: As an open-source, cross-country, spatial analytics solution, AEGIS may globally assess the differences in geographical distribution of health outcomes through the use of standardized geocode and observational health databases.

Lyme disease is a well-recognized public health problem in the USA, however, other tick-borne diseases also have major public health impacts. Yet, limited research has evaluated changes in the spatial and temporal patterns of non-Lyme tick-borne diseases within endemic regions. Using laboratory data from a large healthcare system in north-central Wisconsin from 2000-2016, we applied a Kulldorf's scan statistic to analyze spatial, temporal and seasonal clusters of laboratory-positive cases of human granulocytic anaplasmosis (HGA), babesiosis, and ehrlichiosis at the county level. Older males were identified as the subpopulation at greatest risk for non-Lyme tick-borne diseases and we observed a statistically significant spatial and temporal clustering of cases ( p < 0.05). HGA risk shifted from west to east over time (2000-2016) with a relative risk (RR) ranging from 3.30 to 11.85, whereas babesiosis risk shifted from south to north and west over time (2004-2016) with an RR ranging from 4.33 to 4.81. Our study highlights the occurrence of non-Lyme tick-borne diseases, and identifies at-risk subpopulations and shifting spatial and temporal heterogeneities in disease risk. Our findings can be used by healthcare providers and public health practitioners to increase public awareness and improve case detection.

Remote Indigenous Australians experience disproportionately poor cardio-metabolic health, which is largely underpinned by adverse dietary intake related to social determinants. Little evidence exists about the community environmental-level factors that shape diet quality in this geographically isolated population group. This study aimed to explore the modifiable environmental-level factors associated with the features of dietary intake that underpin cardio-metabolic disease risk in this population group. Community-level dietary intake data were estimated from weekly store sales data collected throughout 2012 and linked with concurrent social, built, and physical environmental dimension data for 13 remote Indigenous Australian communities in the Northern Territory. Statistical analyses were performed to investigate associations. At the community level, store sales of discretionary foods were lower in communities with greater distance to a neighbouring store (r = -0.45 ( p < 0.05)). Sales of sugar-sweetened beverages were lower in communities with higher levels of household crowding (r = -0.55 ( p < 0.05)), higher levels of Indigenous unemployment (r = -0.62 ( p = 0.02)), and greater distance to neighbouring stores (r = -0.61 ( p = 0.004)). Modifiable environmental-level factors may be associated with adverse diet quality in remote Indigenous Australian communities and further investigations of these factors should be considered when developing policies to improve dietary intake quality in geographically isolated populations., Competing Interests: The authors had full access to all data in the study (including statistical reports and tables). The funding sources played no role in the conception, design or conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, and approval of the manuscript. The contents of the published material are solely the responsibility of the individual authors and do not reflect the views of the NHMRC, ARC, NHF, the University of South Australia, Menzies School of Health Research, the University of Canberra, the Queensland University of Technology, or Monash University. The authors report no actual or potential conflicts of interest relevant to this article.

Disasters have substantial consequences for population mental health. We used Twitter to (1) extract negative emotions indicating discomfort in New York City (NYC) before, during, and after Superstorm Sandy in 2012. We further aimed to (2) identify whether pre- or peri-disaster discomfort were associated with peri- or post-disaster discomfort, respectively, and to (3) assess geographic variation in discomfort across NYC census tracts over time. Our sample consisted of 1,018,140 geo-located tweets that were analyzed with an advanced sentiment analysis called "Extracting the Meaning Of Terse Information in a Visualization of Emotion" (EMOTIVE). We calculated discomfort rates for 2137 NYC census tracts, applied spatial regimes regression to find associations of discomfort, and used Moran's I for spatial cluster detection across NYC boroughs over time. We found increased discomfort, that is, bundled negative emotions after the storm as compared to during the storm. Furthermore, pre- and peri-disaster discomfort was positively associated with post-disaster discomfort; however, this association was different across boroughs, with significant associations only in Manhattan, the Bronx, and Queens. In addition, rates were most prominently spatially clustered in Staten Island lasting pre- to post-disaster. This is the first study that determined significant associations of negative emotional responses found in social media posts over space and time in the context of a natural disaster, which may guide us in identifying those areas and populations mostly in need for care.