Spatial analysis and identification of environmental risk factors affecting the distribution of Indoplanorbis and Lymnaea species in semi-arid and irrigated areas of Haryana, India

Fasciolosis, amphistomosis and schistosomosis, transmitted by the freshwater snail species Indoplanorbis and Lymnaea, are important snail-borne diseases in India as they affect the entire spectrum of domestic animals causing substantial mortality and economic loss. Identifying any heterogeneity in the spatial distribution of these snail-borne diseases will allow for targeted disease control and efficient use of resources. The objectives of this study were threefold: (i) to describe and explore the spatial distribution of Indoplanorbis and Lymnaea in Rohtak and Jhajjar districts of Haryana, India (ii) to identify factors associated with occurrence of these freshwater snail species and (iii) to produce a map showing the predicted risk of occurrence of Lymnaea and Indoplanorbis spp. in the study area. Snails were collected from water bodies of 99 settlements out of a total of 453 in the study area. Kernel smoothing was used to generate a kernel ratio map while Kulldorff's spatial scan statistic was used to detect clusters of settlements with a high/low risk. Multivariable logistic regression showed that snails were almost ten times more likely to be present in rice-growing areas than in those not growing rice (OR 9.24) and that snails were less likely to be present with each 1 km increase in distance from a canal (OR 0.86). The regression model was used to produce a map illustrating the predicted risk of snail occurrence. Since the distribution of vector snails mirrors the distribution of snail-borne parasitic diseases, such spatial analysis helps to determine the relative risk of snail-infestation as well as snail-borne diseases' distribution and planning of control activities.

Spatial spread of dengue in a non-endemic tropical city in northern Argentina

After more than eighty years dengue reemerged in Argentina in 1997. Since then, the largest epidemic in terms of geographical extent, magnitude and mortality, was recorded in 2009. In this report we analyzed the DEN-1 epidemic spread in Orán, a mid-size city in a non-endemic tropical area in Northern Argentina, and its correlation with demographic and socioeconomic factors. Cases were diagnosed by ELISA between January and June 2009. We applied a space-time and spatial scan statistic under a Poisson model. Possible association between dengue incidence and socio-economic variables was studied with the Spearman correlation test. The epidemic started from an imported case from Bolivia and space-time analysis detected two clusters: one on February and other in April (in the south and the northeast of the city respectively) with risk ratios of 25.24 and 4.07 (p<0.01). Subsequent cases spread widely around the city without significant space-temporal clustering. Maximum values of the entomological indices were observed in January, at the beginning of the epidemic (B=21.96; LH=8.39). No statistically significant association between socioeconomic variables and dengue incidence was found but positive correlation between population size and the number of cases (p<0.05) was detected. Two mechanisms may explain the observed pattern of epidemic spread in this non-endemic tropical city: a) Short range dispersal of mosquitoes and people generates clusters of cases and b) long-distance (within the city) human movement contributes to a quasi-random distribution of cases.

Spatial analysis of gender variation in the prevalence of hypertension among the middle-aged and elderly population in Zhejiang Province, China

Background

Previous studies have shown that there may be gender disparities in the prevalence of hypertension; however, these studies do not address the spatial information contained in the sample which may limit the analytical results. Our study extends the existing Shared Component Model (SCM) and compares its utility with a logistic regression model to evaluate the significance of spatial information for identifying risk factors for hypertension and other non-rare diseases.

Methods

A total of 1267 residents aged 45 years of age and over were included in our study, of which 48.1 % were males. The overall prevalence of hypertension was 33.2 %, with females experiencing a higher prevalence than males (35.5 % vs. 30.6 %). The research variables included body mass index (BMI), Waist -to-Height Ratio (WHtR), smoking status, alcohol consumption etc. The extended SCM is employed to investigate regional gender variations in the risk of hypertension and assess the gender variation in the middle-aged and elderly populations of Zhejiang Province in eastern China and then its performance is compared with that of a traditional multiple logistic regression model.

Results

Our SCM analysis determined that the spatial pattern of hypertension risk for the middle-aged and elderly populations of Zhejiang Province in eastern China is quite different for males and females. Furthermore, Waist -to-Height Ratio (WHtR) continues to be a simple and effective predictor of hypertension risk for males at the regional level.

Conclusions

We believe that the extended SCM spatial model is a useful tool for identifying risk factors at the regional level.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-016-3121-y) contains supplementary material, which is available to authorized users.

Mapping regional patterns of large forest fires in Wildland-Urban Interface areas in Europe

Over recent decades, Land Use and Cover Change (LUCC) trends in many regions of Europe have reconfigured the landscape structures around many urban areas. In these areas, the proximity to landscape elements with high forest fuels has increased the fire risk to people and property. These Wildland-Urban Interface areas (WUI) can be defined as landscapes where anthropogenic urban land use and forest fuel mass come into contact. Mapping their extent is needed to prioritize fire risk control and inform local forest fire risk management strategies. This study proposes a method to map the extent and spatial patterns of the European WUI areas at continental scale. Using the European map of WUI areas, the hypothesis is tested that the distance from the nearest WUI area is related to the forest fire probability. Statistical relationships between the distance from the nearest WUI area, and large forest fire incidents from satellite remote sensing were subsequently modelled by logistic regression analysis. The first European scale map of the WUI extent and locations is presented. Country-specific positive and negative relationships of large fires and the proximity to the nearest WUI area are found. A regional-scale analysis shows a strong influence of the WUI zones on large fires in parts of the Mediterranean regions. Results indicate that the probability of large burned surfaces increases with diminishing WUI distance in touristic regions like Sardinia, Provence-Alpes-Côte d'Azur, or in regions with a strong peri-urban component as Catalunya, Comunidad de Madrid, Comunidad Valenciana. For the above regions, probability curves of large burned surfaces show statistical relationships (ROC value > 0.5) inside a 5000 m buffer of the nearest WUI. Wise land management can provide a valuable ecosystem service of fire risk reduction that is currently not explicitly included in ecosystem service valuations. The results re-emphasise the importance of including this ecosystem service in landscape valuations to account for the significant landscape function of reducing the risk of catastrophic large fires.

Mapping regional patterns of large forest fires in Wildland-Urban Interface areas in Europe

Over recent decades, Land Use and Cover Change (LUCC) trends in many regions of Europe have reconfigured the landscape structures around many urban areas. In these areas, the proximity to landscape elements with high forest fuels has increased the fire risk to people and property. These Wildland-Urban Interface areas (WUI) can be defined as landscapes where anthropogenic urban land use and forest fuel mass come into contact. Mapping their extent is needed to prioritize fire risk control and inform local forest fire risk management strategies. This study proposes a method to map the extent and spatial patterns of the European WUI areas at continental scale. Using the European map of WUI areas, the hypothesis is tested that the distance from the nearest WUI area is related to the forest fire probability. Statistical relationships between the distance from the nearest WUI area, and large forest fire incidents from satellite remote sensing were subsequently modelled by logistic regression analysis. The first European scale map of the WUI extent and locations is presented. Country-specific positive and negative relationships of large fires and the proximity to the nearest WUI area are found. A regional-scale analysis shows a strong influence of the WUI zones on large fires in parts of the Mediterranean regions. Results indicate that the probability of large burned surfaces increases with diminishing WUI distance in touristic regions like Sardinia, Provence-Alpes-Côte d'Azur, or in regions with a strong peri-urban component as Catalunya, Comunidad de Madrid, Comunidad Valenciana. For the above regions, probability curves of large burned surfaces show statistical relationships (ROC value > 0.5) inside a 5000 m buffer of the nearest WUI. Wise land management can provide a valuable ecosystem service of fire risk reduction that is currently not explicitly included in ecosystem service valuations. The results re-emphasise the importance of including this ecosystem service in landscape valuations to account for the significant landscape function of reducing the risk of catastrophic large fires.

Using spatially explicit indicators to investigate watershed characteristics and stream temperature relationships

We generate a series of novel indicators of spatially explicit watershed permeability and runoff characteristics to examine the relationship between land cover and water temperature parameters in a rapidly urbanizing watershed. Our framework provides a readily adaptable method to examine the thermal sensitivity of streams based upon the underlying geomorphological and surface characteristics of drainage basins. Using four model groups each using a different landscape characteristic weighting scheme (Model Group 1: areal averages; Model Group 2: inverse distance by total flow length; Model Group 3: overland distance to stream network and distance squared; Model Group 4: proportional flow accumulation), we examined the predictive capacity of 19 variables, including combinations of simplified land cover, elevation, slope, and flow accumulation, on five stream thermal properties: seven day moving average of daily minimum and maximum, seasonal mean temperature, a novel metric of thermal 'flashiness', and total days with maximum temperature exceeding 17.8°C. We find that the use of spatially explicit landscape indicators combining watershed processes improves the performance of regressions for predicting a number of ecologically relevant stream temperature variables. Improved indicators of watershed condition lend themselves for rapid investigation of the relationship between stream thermal conditions and landscape characteristics in watersheds modified by human land uses, ultimately providing a more hydrologically meaningful indicator for the impacts of landscape change.

Risks of developing breast and colorectal cancer in association with incomes and geographic locations in Texas: a retrospective cohort study

BACKGROUND

No study has been conducted to investigate the spatial pattern and association of socioeconomic status (such as income) with breast and colorectal cancer incidence in Texas, United States. This study aimed to determine whether median household income was associated with the risk of developing breast and colorectal cancer in Texas and to identify higher cancer risks by race/ethnicity and geographic areas.

METHODS

This was a retrospective cohort study with an ecological component in using aggregated measures at the county level. We identified 243,677 women with breast cancer and 155,534 men and women with colorectal cancer residing in 254 counties in Texas in 1995-2011 from the public-use dataset of Texas Cancer Registry. The denominator population and median household income at the county level was obtained from the U.S. Bureau of the Census. Cancer incidence rates were calculated as number of cases per 100,000 persons and age-adjusted using the 2000 US population data. We used the ArcGIS v10.1 (geographic information system software) to identify multiple clustered counties with high and low cancer incidences in Texas.

RESULTS

Age-adjusted breast cancer incidence rate in the highest median income quintile group was 151.51 cases per 100,000 in 2008-2011 as compared to 98.95 cases per 100,000 in the lowest median income quintile group. The risk of colorectal cancer appeared to decrease with increasing median income in racial/ethnic population. Spatial analysis revealed the significant low breast cancer incidence cluster regions located in southwest US-Mexico border counties in Texas.

CONCLUSIONS

This study demonstrated that higher income was associated with an increased risk of breast cancer and a decreased risk of colorectal cancer in Texas. There were geographic variations with cancer incidence clustered in high risk areas in Texas. Future studies may need to explore more factors that might explain income and cancer risk associations and their geographic variations.

Liver fluke (Fasciola hepatica) infection in cattle in Northern Ireland: a large-scale epidemiological investigation utilising surveillance data

Background

Liver fluke (Fasciola hepatica) is a widespread parasite of ruminants which can have significant economic impact on cattle production. Fluke infection status at the animal-level is captured during meat inspection of all animals processed for human consumption within Northern Ireland. These national datasets have not been analysed to assess their utility in uncovering patterns in fluke infection at animal- and herd-levels in Northern Ireland.

Methods

We utilised a dataset of 1.2 million animal records from ~18,000 herds across 3 years (2011–2013) to assess animal- and herd-level apparent prevalence and risk-factors associated with fluke infection. Animal-level apparent prevalence was measured as the proportion of animals exhibiting evidence of fluke infection at slaughter; between herd-level infection prevalence was measured by binary categorisation of herds (infected or not). “Within herd” infection prevalence was measured using the proportion of animals within a herd that showed evidence of fluke infection per year (ranging from 0–100 %). “Within herd” infection prevalence at the herd level was investigated using multivariable modelling.

Results

At the animal level, the proportion of animals slaughtered that exhibited evidence of infection was 21–25 % amongst years. Across herds, the proportion of herds with at least one infected animal, varied between 61 and 65 %. However, there was a significant sampling effect at the herd-level; all herds where at least 105 animals slaughtered over the study period exhibited evidence of fluke infection (100 %). There was significant variation in terms of within-herd infection prevalence. Risk factors included herd type, long-term weather variation, geographic location (region) and the abattoir.

Conclusions

Liver fluke apparent prevalence was high at the herd-level across years. However, there was lower prevalence at the animal level, which may indicate significant variation in the exposure to fluke infection within herds. The proportion infected within-herds varied significantly in time and space, and by abattoir, herd-type and some weather variables. These data are a useful source of information on a widespread endemic disease, despite known limitations in terms of test performance (low sensitivity). As well as informing on the distribution and severity of liver fluke infection, these analyses will be used to investigate the effect of co-infection on risk for bovine tuberculosis.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-016-1489-2) contains supplementary material, which is available to authorized users.

Trends and spatial patterns of mortality related to neglected tropical diseases in Brazil

We analysed nationwide trends and spatial distribution of NTD-related mortality in Brazil. We included all death certificates in Brazil from 2000 to 2011, in which NTDs were recorded as any causes of death. A total of 100,814/12,491,280 (0.81%) death certificates were identified, which mentioned at least one NTD. Age-adjusted NTD-related mortality rates showed a significant decrease over time (annual percent change [APC]: − 2.1%; 95% CI: − 2.8 to − 1.3), with decreasing mortality rates in the Southeast, South, and Central-West regions, stability in the Northeast region, and increase in the North region. We identified spatial and spatiotemporal high-risk clusters for NTD-related mortality in all regions, with a major cluster covering a wide geographic range in central Brazil. Despite nationwide decrease of NTD-related mortality in the observation period, regional differences remain, with increasing mortality trends especially in the socioeconomically disadvantaged regions of the country. The existence of clearly defined high-risk areas for NTD-related deaths reinforces the need for integrated prevention and control measures in areas with highest disease burden.

Spatio-Temporal Pattern of Breast Cancer - Case Study of Southern Karnataka, India

INTRODUCTION

Spatio-ecological study of disease provides a framework to study the interaction of genetic, environmental, social, cultural and behavioural factors on people's health. The occurrence and interaction of these factors are different in different places, giving rise to distinct geographic or spatial variation. Diseases like breast cancer have variation both spatially and temporally. Public health practitioners can use Geographic Information System (GIS) as a visualization tool to effectively present geographic phenomenon and depict it in maps that might remain otherwise undiscovered in tabular form.

AIM

To demonstrate how GIS can be used to understand and communicate breast cancer data through spatial visualization techniques.

OBJECTIVES

(i) To visualize the Spatial Distribution of Breast cancer incidences by a point map. (ii) To visualize the Temporal distribution of breast cancer incidences by thematic maps for the study period of 2007 -2011.

MATERIALS AND METHODS

Total 1090 breast cancer case records collected for the year 2007-2012 were segregated taluk wise for the 29 taluks and geocoded using the address of the patient, creating a point map. ArcGIS 10.2 software was used to prepare thematic map of breast cancer cases. The taluk wise aggregated breast cancer incidence from the year 2007 to 2011 was then attributed into polygon map representing taluks (Base Map). Natural break data classification technique was used to classify the breast cancer incidence data and breast cancer incidences were classified as low, moderate, high and very high.

RESULTS

Spatial distribution of breast cancer incidences using thematic mapping methods high incidences were reported in MY_ T24 (Hunsur), MY_ T25 (KR Nagar), MY_27 (Nanjangud), CH_T1 (Chamrajnagar) and CH-T2 (Gundlupet). Temporal maps prepared for the study from 2007 to 2011 showed that Mysore Taluk had very high Incidence level and the same was observed throughout the study period. The taluks which have high and moderate intensities seem to be fluctuating. However, 25 taluks do not fall into very high category during the study period. Taluks such Gundlupet (CH_T2), K R Nagar (MY_T25), Kollegal (CH_T3) have been observed to enter high intensity category during the year 2011 from moderate intensity. It is also observed that Nanjangud (MY_T27) is in high intensity category throughout the study period which might be due to its proximity to Mysore urban.

CONCLUSION

Analysis of Breast Cancer in southern Karnataka using GIS has revealed that urban areas of Mysore has the highest risk of breast cancer and the temporal trends reveal that even rural areas with moderate risk are moving towards high risk areas.

Spatial analysis and source profiling of beta-agonists and sulfonamides in Langat River basin, Malaysia

Beta-agonists and sulfonamides are widely used for treating both humans and livestock for bronchial and cardiac problems, infectious disease and even as growth promoters. There are concerns about their potential environmental impacts, such as producing drug resistance in bacteria. This study focused on their spatial distribution in surface water and the identification of pollution sources in the Langat River basin, which is one of the most urbanized watersheds in Malaysia. Fourteen beta-agonists and 12 sulfonamides were quantitatively analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). A geographic information system (GIS) was used to visualize catchment areas of the sampling points, and source profiling was conducted to identify the pollution sources based on a correlation between a daily pollutant load of the detected contaminant and an estimated density of human or livestock population in the catchment areas. As a result, 6 compounds (salbutamol, sulfadiazine, sulfapyridine, sulfamethazine, sulfadimethoxine and sulfamethoxazole) were widely detected in mid catchment areas towards estuary. The source profiling indicated that the pollution sources of salbutamol and sulfamethoxazole were from sewage, while sulfadiazine was from effluents of cattle, goat and sheep farms. Thus, this combination method of quantitative and spatial analysis clarified the spatial distribution of these drugs and assisted for identifying the pollution sources.

Is shrimp farming a successful adaptation to salinity intrusion? A geospatial associative analysis of poverty in the populous Ganges-Brahmaputra-Meghna Delta of Bangladesh

The Ganges-Brahmaputra-Meghna delta of Bangladesh is one of the most populous deltas in the world, supporting as many as 140 million people. The delta is threatened by diverse environmental stressors including salinity intrusion, with adverse consequences for livelihood and health. Shrimp farming is recognised as one of the few economic adaptations to the impacts of the rapidly salinizing delta. Although salinity intrusion and shrimp farming are geographically co-located in the delta, there has been no systematic study to examine their geospatial associations with poverty. In this study, we use multiple data sources including Census, Landsat Satellite Imagery and soil salinity survey data to examine the extent of geospatial clustering of poverty within the delta and their associative relationships with salinity intensity and shrimp farming. The analysis was conducted at the union level, which is the lowest local government administrative unit in Bangladesh. The findings show a strong clustering of poverty in the delta, and whilst different intensities of salinization are significantly associated with increasing poverty, neither saline nor freshwater shrimp farming has a significant association with poverty. These findings suggest that whilst shrimp farming may produce economic growth, in its present form it has not been an effective adaptation for the poor and marginalised areas of the delta. The study demonstrates that there are a series of drivers of poverty in the delta, including salinization, water logging, wetland/mudflats, employment, education and access to roads, amongst others that are discernible spatially, indicating that poverty alleviation programmes in the delta require strengthening with area-specific targeted interventions.

The spatial distribution of gender differences in obesity prevalence differs from overall obesity prevalence among US adults

PURPOSE

Although obesity disparities between racial and socioeconomic groups have been well characterized, those based on gender and geography have not been as thoroughly documented. This study describes obesity prevalence by state, gender, and race and/or ethnicity to (1) characterize obesity gender inequality, (2) determine if the geographic distribution of inequality is spatially clustered, and (3) contrast the spatial clustering patterns of obesity gender inequality with overall obesity prevalence.

METHODS

Data from the Centers for Disease Control and Prevention's 2013 Behavioral Risk Factor Surveillance System were used to calculate state-specific obesity prevalence and gender inequality measures. Global and local Moran's indices were calculated to determine spatial autocorrelation.

RESULTS

Age-adjusted, state-specific obesity prevalence difference and ratio measures show spatial autocorrelation (z-score = 4.89, P-value < .001). Local Moran's indices indicate the spatial distributions of obesity prevalence and obesity gender inequalities are not the same. High and low values of obesity prevalence and gender inequalities cluster in different areas of the United States.

CONCLUSIONS

Clustering of gender inequality suggests that spatial processes operating at the state level, such as occupational or physical activity policies or social norms, are involved in the etiology of the inequality and necessitate further attention to the determinates of obesity gender inequality.

Ecological context of infant mortality in high-focus states of India

OBJECTIVES

This goal of this study was to shed light on the ecological context as a potential determinant of the infant mortality rate in nine high-focus states in India.

METHODS

Data from the Annual Health Survey (2010-2011), the Census of India (2011), and the District Level Household and Facility Survey 3 (2007-08) were used in this study. In multiple regression analysis explanatory variable such as underdevelopment is measured by the non-working population, and income inequality, quantified as the proportion of households in the bottom wealth quintile. While, the trickle-down effect of education is measured by female literacy, and investment in health, as reflected by neonatal care facilities in primary health centres.

RESULTS

A high spatial autocorrelation of district infant mortality rates was observed, and ecological factors were found to have a significant impact on district infant mortality rates. The result also revealed that non-working population and income inequality were found to have a negative effect on the district infant mortality rate. Additionally, female literacy and new-born care facilities were found to have an inverse association with the infant mortality rate.

CONCLUSIONS

Interventions at the community level can reduce district infant mortality rates.

Repeated praziquantel treatments remodel the genetic and spatial landscape of schistosomiasis risk and transmission

Repeated treatments with praziquantel reduce schistosomiasis prevalence and morbidity, but transmission persists and populations often recover within a few years. To identify factors associated with persistence, we surveyed and treated all identified Schistosoma mansoni infections in two rural Brazilian communities (Jenipapo and Volta do Rio) in 2009, 2012 and 2013. Eggs were collected from all infected individuals and genotyped with 11 microsatellite markers to evaluate parasite differentiation and diversity. After successive rounds of community-wide treatment, prevalence decreased from 45% to 24% then 16%. Intensity of infection decreased by 57% over this period, and the number of eggs transmitted to the environment decreased by 92%. During all time periods the majority of eggs were excreted by those >15years of age. The incidence was 23% in 2012 and 15% in 2013, consistent with a decrease in transmission. There was little immigration or gene flow over a distance of 6km. On reinfection, infrapopulations were moderately differentiated indicating that pretreatment multilocus genotypes were not fully reacquired. The effective population size responded to census population decline more rapidly than differentiation. Reinfection was concentrated in the downstream portion of Jenipapo, consistent with the observed increased human fecal contamination. At this scale and in this area S. mansoni infections exist on a fragmented landscape with a highly focal pattern of transmission that may facilitate future elimination.

Imaging collective cell migration and hair cell regeneration in the sensory lateral line

The accessibility of the lateral line system and its amenability to long-term in vivo imaging transformed the developing lateral line into a powerful model system to study fundamental morphogenetic events, such as guided migration, proliferation, cell shape changes, organ formation, organ deposition, cell specification and differentiation. In addition, the lateral line is not only amenable to live imaging during migration stages but also during postembryonic events such as sensory organ tissue homeostasis and regeneration. The robust regenerative capabilities of the mature, mechanosensory lateral line hair cells, which are homologous to inner ear hair cells and the ease with which they can be imaged, have brought zebrafish into the spotlight as a model to develop tools to treat human deafness. In this chapter, we describe protocols for long-term in vivo confocal imaging of the developing and regenerating lateral line.

Spatial epidemiological analysis of bovine encephalomyelitis outbreaks caused by Akabane virus infection in western Japan in 2011

Akabane disease, which is distributed in temperate and tropical regions in the world, is a vector-borne disease of ruminants caused by the Akabane virus, transmitted by Culicoides biting midges. In 2011, outbreaks of Akabane viral encephalomyelitis occurred in the Shimane Prefecture in western Japan. In this study, a spatial epidemiological analysis was conducted to understand environmental factors associated with the spread of Akabane disease. By applying a conditional autoregressive model, the relationship between infection and environmental variables was explored. The results showed that the dominance of farmlands and the presence of infected farms within a 3-km radius had a significant effect on infection. This result implies that land use, which would relate with the vector habitat, and the presence of neighboring infected farms as a source of infection may have influenced the spread of the disease in this region. These findings provide basic insights into the spread of Akabane disease and useful suggestions for developing a surveillance program and preventive measures against the disease.

Spatial variation in the ecological relationships among the components of Beijing's carbon metabolic system

In this paper, we construct a spatially explicit model of carbon metabolism for the flows of carbon among the components of an urban area. We used the model to identify spatial heterogeneity in the ecological relationships within a carbon metabolic network. We combined land-use and cover type maps for Beijing from 1990 to 2010 with empirical coefficients and socioeconomic data to quantify the flows. We used utility analysis to determine the ecological relationships between the components of the system and analyzed their changes during urban development. We used ArcGIS to analyze their spatial variation. We found that the positive utilities in Beijing decreased over time and that negative relationships mostly outweighed positive relationships after 2000. The main ecological relationships were distributed throughout the entire urban area before 2000; subsequently, exploitation, control, and mutualism relationships became concentrated in the southeast, leaving competition relationships to dominate the northwest. Mutualism relationships were most common for natural components, but were not stable because they were easily disturbed by urban expansion. Transportation and industrial land and urban land were the most important contributors to exploitation and control relationships and may be important indicators of spatial adjustment. Increasing competition relationships unbalanced the carbon metabolism, and limitations on the area of land available for development and on the water resources led to increasingly serious competition. The results provide an objective basis for planning adjustments to Beijing's land-use patterns to improve its carbon metabolism and reduce carbon emission.

Estimating nitrogen oxides emissions at city scale in China with a nightlight remote sensing model

Increasing nitrogen oxides (NOx) emissions over the fast developing regions have been of great concern due to their critical associations with the aggravated haze and climate change. However, little geographically specific data exists for estimating spatio-temporal trends of NOx emissions. In order to quantify the spatial and temporal variations of NOx emissions, a spatially explicit approach based on the continuous satellite observations of artificial nighttime stable lights (NSLs) from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) was developed to estimate NOx emissions from the largest emission source of fossil fuel combustion. The NSL based model was established with three types of data including satellite data of nighttime stable lights, geographical data of administrative boundaries, and provincial energy consumptions in China, where a significant growth of NOx emission has experienced during three policy stages corresponding to the 9th-11th)Five-Year Plan (FYP, 1995-2010). The estimated national NOx emissions increased by 8.2% per year during the study period, and the total annual NOx emissions in China estimated by the NSL-based model were approximately 4.1%-13.8% higher than the previous estimates. The spatio-temporal variations of NOx emissions at city scale were then evaluated by the Moran's I indices. The global Moran's I indices for measuring spatial agglomerations of China's NOx emission increased by 50.7% during 1995-2010. Although the inland cities have shown larger contribution to the emission growth than the more developed coastal cities since 2005, the High-High clusters of NOx emission located in Beijing-Tianjin-Hebei regions, the Yangtze River Delta, and the Pearl River Delta should still be the major focus of NOx mitigation. Our results indicate that the readily available DMSP/OLS nighttime stable lights based model could be an easily accessible and effective tool for achieving strategic decision making toward NOx reduction.

Measuring Community Resilience to Coastal Hazards along the Northern Gulf of Mexico

The abundant research examining aspects of social-ecological resilience, vulnerability, and hazards and risk assessment has yielded insights into these concepts and suggested the importance of quantifying them. Quantifying resilience is complicated by several factors including the varying definitions of the term applied in the research, difficulties involved in selecting and aggregating indicators of resilience, and the lack of empirical validation for the indices derived. This paper applies a new model, called the resilience inference measurement (RIM) model, to quantify resilience to climate-related hazards for 52 U.S. counties along the northern Gulf of Mexico. The RIM model uses three elements (exposure, damage, and recovery indicators) to denote two relationships (vulnerability and adaptability), and employs both K-means clustering and discriminant analysis to derive the resilience rankings, thus enabling validation and inference. The results yielded a classification accuracy of 94.2% with 28 predictor variables. The approach is theoretically sound and can be applied to derive resilience indices for other study areas at different spatial and temporal scales.

Neighborhood-Level and Spatial Characteristics Associated with Lay Naloxone Reversal Events and Opioid Overdose Deaths

There were over 23,000 opioid overdose deaths in the USA in 2013, and opioid-related mortality is increasing. Increased access to naloxone, particularly through community-based lay naloxone distribution, is a widely supported strategy to reduce opioid overdose mortality; however, little is known about the ecological and spatial patterns of the distribution and utilization of lay naloxone. This study aims to investigate the neighborhood-level correlates and spatial relationships of lay naloxone distribution and utilization and opioid overdose deaths. We determined the locations of lay naloxone distribution sites and the number of unintentional opioid overdose deaths and reported reversal events in San Francisco census tracts (n = 195) from 2010 to 2012. We used Wilcoxon rank-sum tests to compare census tract characteristics across tracts adjacent and not adjacent to distribution sites and multivariable negative binomial regression models to assess the association between census tract characteristics, including distance to the nearest site, and counts of opioid overdose deaths and naloxone reversal events. Three hundred forty-two opioid overdose deaths and 316 overdose reversals with valid location data were included in our analysis. Census tracts including or adjacent to a distribution site had higher income inequality, lower percentage black or African American residents, more drug arrests, higher population density, more overdose deaths, and more reversal events (all p < 0.05). In multivariable analysis, greater distance to the nearest distribution site (up to a distance of 4000 m) was associated with a lower count of Naloxone reversals [incidence rate ratio (IRR) = 0.51 per 500 m increase, 95% CI 0.39-0.67, p < 0.001] but was not significantly associated with opioid overdose deaths. These findings affirm that locating lay naloxone distribution sites in areas with high levels of substance use and overdose risk facilitates reversals of opioid overdoses in those immediate areas but suggests that alternative delivery methods may be necessary to reach individuals in other areas with less concentrated risk.

Neurocysticercosis-related mortality in Brazil, 2000-2011: Epidemiology of a neglected neurologic cause of death

Neurocysticercosis (NCC) is an important cause of severe neurological disease mainly in low- and middle-income countries, but data on NCC mortality from endemic areas are scarce. Here we analysed the epidemiological patterns of NCC-related mortality in Brazil. We included all deaths recorded in Brazil between 2000 and 2011, in which NCC was mentioned on death certificates, either as underlying or as associated cause of death. NCC was identified in 1829/12,491,280 deaths (0.015%), 1130 (61.8%) as underlying cause, and 699 (38.2%) as associated cause. Overall age-adjusted mortality rate for the period was 0.97 deaths/1,000,000 inhabitants (95% confidence interval [CI]: 0.83-1.12). The highest NCC-related mortality rates were found in males, elderly, white race/colour and residents in endemic states/regions. Age-adjusted mortality rates at national level decreased significantly over time (annual percent change [APC]: -4.7; 95% CI: -6.0 to -3.3), with a decrease in the Southeast, South and Central-West regions, and a non-significant increasing trend in the North and Northeast regions. We identified spatial and spatiotemporal high-risk mortality clusters located mainly in NCC-endemic areas. Conditions related to the nervous system were the most commonly associated causes of death when NCC was mentioned as an underlying cause, and HIV/AIDS was the main underlying cause when NCC was an associated cause. NCC is a neglected and preventable cause of severe neurologic disease and death with high public health impact in Brazil. There is a clear need to strengthen nationwide epidemiological surveillance and control for the taeniasis/cysticercosis complex.

Quantitative spatial analysis of transcripts in multinucleate cells using single-molecule FISH

mRNA positioning in the cell is important for diverse cellular functions and proper development of multicellular organisms. Single-molecule RNA FISH (smFISH) enables quantitative investigation of mRNA localization and abundance at the level of individual molecules in the context of cellular features. Details about spatial mRNA patterning at various times, in different genetic backgrounds, at different developmental stages, and under varied environmental conditions provide invaluable insights into the mechanisms and functions of spatial regulation. Here, we describe detailed methods for performing smFISH along with immunofluorescence for two large, multinucleate cell types: the fungus Ashbya gossypii and cultured mouse myotubes. We also put forward a semi-automated image processing tool that systematically detects mRNAs from smFISH data and statistically analyzes the spatial pattern of mRNAs using a customized MATLAB code. These protocols and image analysis tools can be adapted to a wide variety of transcripts and cell types for systematically and quantitatively analyzing mRNA distribution in three-dimensional space.

Interpreting Intra-site Spatial Patterns in Seasonal Contexts: an Ethnoarchaeological Case Study from the Western Alps

This paper deals with the ethnoarchaeological analysis of the spatial pattern of artefacts and ecofacts within two traditional pastoral huts (a dwelling and a seasonal dairy) in the uplands of Val Maudagna (Cuneo province, Italian western Alps). The composition of the ethnoarchaeological assemblages of the two huts was studied and compared; point pattern analysis was applied to identify spatial processes mirrored in the interactions between objects; Moran's I correlogram and empirical variogram were used to investigate the effects of trampling on the displacement of objects on the floor. The results were compared with information provided by the herder who still used the huts. The quantitative and ethnographical data enabled inferences to be made that can help in the interpretation of archaeological seasonal sites. The function of a seasonal site can be recognized, as can the impact of delayed curation on the composition of the assemblage and the importance of the intensity of occupation compared with the frequency of occupation. The spatial organization of activities is reflected in the spatial patterns of objects, with clearer identification of activity areas in intensively occupied sites, and there is evidence for the behaviour behind the spatial segregation of activities. Trampling is a crucial post-depositional factor in the displacement of artefacts and ecofacts, especially in non-intensively exploited sites. From a methodological point of view, this research is another example that highlights the importance of integrating quantitative methods (especially spatial analysis and geostatistical methods) and ethnoarchaeological data in order to improve the interpretation of archaeological sites and assemblages.

Identifying spatial and seasonal patterns of river water quality in a semiarid irrigated agricultural Mediterranean basin

A detailed understanding of the study area is essential to achieve key information and optimize the monitoring, analysis, and evaluation of water quality of natural ecosystems that have been highly transformed into agricultural areas. Using classification techniques like the hierarchical cluster analysis (CA) and partial triadic analysis (PTA), we assessed the sources of water pollution and the seasonal influence of human activities in water composition in a river basin from northeastern Spain. The results suggested that a strong connection existed between water quality and the seasonality of the human activities. The CA showed the spatial relationship between water chemistry and the adjacent land uses. The PTA associated the analyzed variables to their pollutant source. Electrical conductivity (EC), Cl(-), SO4(2-)-S, Na(+), and Mg(2+) ions were related with agricultural sources, whereas NH4(+)-N, PT, and PO4(3-)-P were linked with urban polluted sites. Concentration of NO3(-)-N was associated with urban land use. Differences in water composition according to the irrigation intensity were also found during the irrigation season. The statistical tools used in this work, especially the PTA, allowed us to jointly analyze the spatial and seasonal components of water pollutant trends. We obtained a more comprehensive knowledge of water quality patterns in the study area, which will be essential when taking measures to minimize the effects of water pollution.

Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing

Natural and semi-natural habitats in agricultural landscapes are likely to come under increasing pressure with the global population set to exceed 9 billion by 2050. These non-cropped habitats are primarily made up of trees, hedgerows and grassy margins and their amount, quality and spatial configuration can have strong implications for the delivery and sustainability of various ecosystem services. In this study high spatial resolution (0.5 m) colour infrared aerial photography (CIR) was used in object based image analysis for the classification of non-cropped habitat in a 10,029 ha area of southeast England. Three classification scenarios were devised using 4 and 9 class scenarios. The machine learning algorithm Random Forest (RF) was used to reduce the number of variables used for each classification scenario by 25.5 % ± 2.7%. Proportion of votes from the 4 class hierarchy was made available to the 9 class scenarios and where the highest ranked variables in all cases. This approach allowed for misclassified parent objects to be correctly classified at a lower level. A single object hierarchy with 4 class proportion of votes produced the best result (kappa 0.909). Validation of the optimum training sample size in RF showed no significant difference between mean internal out-of-bag error and external validation. As an example of the utility of this data, we assessed habitat suitability for a declining farmland bird, the yellowhammer (Emberiza citronella), which requires hedgerows associated with grassy margins. We found that ∼22% of hedgerows were within 200 m of margins with an area >183.31 m2. The results from this analysis can form a key information source at the environmental and policy level in landscape optimisation for food production and ecosystem service sustainability.

Childhood lead exposure and sexually transmitted infections: New evidence

INTRODUCTION

The adverse health effects of lead exposure in children are well documented and include intellectual and behavioral maladies. Childhood lead exposure has also been linked to impulsive behaviors, which, in turn, are associated with a host of negative health outcomes including an increased risk for sexually transmitted infections (STI). The purpose of this study was to assess the association of lead exposure with STI rates across census tracts in St. Louis City, Missouri.

METHODS

Incident cases of gonorrhea and chlamydia (GC) during 2011 were identified from the Missouri Department of Health and Senior Services and aggregated by census tract. We also geocoded the home address of 59,645 children >72 months in age who had blood lead level tests performed in St. Louis City from 1996 to 2007. Traditional regression and Bayesian spatial models were used to determine the relationship between GC and lead exposure while accounting for confounders (condom and alcohol availability, crime, and an index of concentrated disadvantage).

RESULTS

Incident GC rates were found to cluster across census tracts (Moran's I=0.13, p=0.006). After accounting for confounders and their spatial dependence, a linear relationship existed between lead exposure and GC incidence across census tracts, with higher GC rates occurring in the northern part of St. Louis City

CONCLUSIONS

At the census-tract level, higher lead exposure is associated with higher STI rates. Visualizing these patterns through maps may help deliver targeted interventions to reduce geographic disparities in GC rates.

Manuscript title: Geospatial analysis of Cancer risk and residential proximity to coal mines in Illinois

BACKGROUND

Studies have indicated a population-level association between coal mining and cancer incidence and mortality, but few studies specifically examined residential proximity to this exposure using spatial analysis. We utilized a Geographic Information Systems (GIS) approach to perform spatial and statistical analyses to test two coal mining exposure variables and their associations with cancer incidence and mortality in Illinois--the fourth highest coal producing state in the United States.

METHODS

Data included age-adjusted county-level cancer incidence and mortality for five cancers: all malignant, lung, colorectal, breast (female) and prostate. Coal mining exposure was defined by two variables: coal production group and distance-weighted exposure. Spatial analyses were performed to identify spatial clustering. Correlation and stepwise regression analyses were performed to explore the relationship between cancer incidence and mortality and coal mining exposures. Covariates considered in regression analyses included socioeconomic deprivation, former/current smoking prevalence, race, and rurality.

RESULTS

Global spatial autocorrelation indicated significant spatial clustering of incidence, mortality and aggregated coal production. Distance-weighted exposure was significantly correlated with coal production group, age-adjusted all cancer incidence and age-adjusted all cancer mortality. Regression analyses indicated an association between recent coal production and colorectal cancer incidence (p=0.009) and mortality (p=0.035) and prostate cancer mortality (p=0.047). Distance weighted exposure was associated with lung cancer incidence (p=0.004) and mortality (p<0.001), and all cancer mortality (p<0.001).

CONCLUSION

Coal production, incidence and mortality are spatially clustered in Illinois. Exposures to coal mining were associated with elevated risk of multiple cancers, most notably lung and colorectal. The environmental impact of the mining industry is substantial, and exposure of individuals residing near coal mines to known carcinogens is plausible. Future studies are needed to further elucidate the population exposure dynamics of coal mining, and should be explored using individual-level exposures and cancer outcomes.

High-resolution spatial analysis of cholera patients reported in Artibonite department, Haiti in 2010-2011

BACKGROUND

Cholera is caused by Vibrio cholerae, and is transmitted through fecal-oral contact. Infection occurs after the ingestion of the bacteria and is usually asymptomatic. In a minority of cases, it causes acute diarrhea and vomiting, which can lead to potentially fatal severe dehydration, especially in the absence of appropriate medical care. Immunity occurs after infection and typically lasts 6-36 months. Cholera is responsible for outbreaks in many African and Asian developing countries, and caused localised and episodic epidemics in South America until the early 1990s. Haiti, despite its low socioeconomic status and poor sanitation, had never reported cholera before the recent outbreak that started in October 2010, with over 720,000 cases and over 8700 deaths (Case fatality rate: 1.2%) through 8 december 2014. So far, this outbreak has seen 3 epidemic peaks, and it is expected that cholera will remain in Haiti for some time.

METHODOLOGY/FINDINGS

To trace the path of the early epidemic and to identify hot spots and potential transmission hubs during peaks, we examined the spatial distribution of cholera patients during the first two peaks in Artibonite, the second-most populous department of Haiti. We extracted the geographic origin of 84,000 patients treated in local health facilities between October 2010 and December 2011 and mapped these addresses to 63 rural communal sections and 9 urban cities. Spatial and cluster analysis showed that during the first peak, cholera spread along the Artibonite River and the main roads, and sub-communal attack rates ranged from 0.1% to 10.7%. During the second peak, remote mountain areas were most affected, although sometimes to very different degrees even in closely neighboring locations. Sub-communal attack rates during the second peak ranged from 0.2% to 13.7%. The relative risks at the sub-communal level during the second phase showed an inverse pattern compared to the first phase.

CONCLUSION/SIGNIFICANCE

These findings demonstrate the value of high-resolution mapping for pinpointing locations most affected by cholera, and in the future could help prioritize the places in need of interventions such as improvement of sanitation and vaccination. The findings also describe spatio-temporal transmission patterns of the epidemic in a cholera-naïve country such as Haiti. By identifying transmission hubs, it is possible to target prevention strategies that, over time, could reduce transmission of the disease and eventually eliminate cholera in Haiti.

[Spatial analysis of road traffic accidents with fatalities in Spain, 2008-2011]

OBJECTIVE

To estimate the areas of greatest density of road traffic accidents with fatalities at 24 hours per km(2)/year in Spain from 2008 to 2011, using a geographic information system.

METHODS

Accidents were geocodified using the road and kilometer points where they occurred. The average nearest neighbor was calculated to detect possible clusters and to obtain the bandwidth for kernel density estimation.

RESULTS

A total of 4775 accidents were analyzed, of which 73.3% occurred on conventional roads. The estimated average distance between accidents was 1,242 meters, and the average expected distance was 10,738 meters. The nearest neighbor index was 0.11, indicating that there were aggregations of accidents in space. A map showing the kernel density was obtained with a resolution of 1 km(2), which identified the areas of highest density.

CONCLUSIONS

This methodology allowed a better approximation to locating accident risks by taking into account kilometer points. The map shows areas where there was a greater density of accidents. This could be an advantage in decision-making by the relevant authorities.

Lymphatic filariasis among children and adolescents: spatial identification via socio-environmental indicators to define priority areas for elimination

BACKGROUND

The occurrence of lymphatic filariasis (LF) and its transmission dynamics among children and adolescents are still not clearly elucidated. The aim of this study was to describe the spatial distribution of microfilaremia and its relationship with socio-environmental variables, thereby identifying areas at greater risk of transmission.

METHODS

An ecological study was conducted, in which the analysis unit was formed by districts of the municipality of Jaboatão dos Guararapes in the state of Pernambuco, Brazil. The data source was a parasitological survey. Inadequate sewage disposal, number of people in the household and income of between 0.5 and 1 minimum salary were used to construct the socio-environmental indicator. The districts were then grouped according to the indicator into three risk strata, using the k-means clustering technique.

RESULTS

In the study, 96/8670 (1.1%) of the population were found to present microfilaremia. The high-risk stratum had a mean prevalence rate of 1.9% and a risk of 2.56 (p<0.05), in relation to the low-risk stratum. The middle stratum grouped the households that presented an intermediate risk of transmission, with a prevalence rate of 1.92%.

CONCLUSIONS

The indicator used is a promising tool that enables the precise measurement of the relationship between social deprivation and the prevalence of filarial infection among children. Thus, it can be used to plan control and elimination actions.

The effect of spatial barriers on realised accessibility to heath services after a natural disaster

The closure of the Manawatu Gorge in New Zealand in August 2011 caused a change in the travel time for patients living in the east of the MidCentral Health District to their health services located in Palmerston North. This presented an opportunity to study the effect a change in spatial access had on a population before and after such an event. We used a retrospective cohort design with routinely collected data from general practice and hospital services. Realised accessibility was calculated for 101,456 patients over 3.5 years. General practice utilization appeared to be the only service affected negatively during the gorge closure (rate ratio 1.106). Outpatient attendances had an increase in use by those with increased travel time (rate ratio 0.922). There was evidence of other unidentified factors that impacted the use of services across both intervention and control groups between the gorge open and closed periods. These results were more conservative than those produced by a traditional uncontrolled travel time category analysis which suggested a correlation in non-urgent ED attendance and general practice and boundary effects in all ED attendances and hospital admissions.

Accounting for spatial effects in land use regression for urban air pollution modeling

In order to accurately assess air pollution risks, health studies require spatially resolved pollution concentrations. Land-use regression (LUR) models estimate ambient concentrations at a fine spatial scale. However, spatial effects such as spatial non-stationarity and spatial autocorrelation can reduce the accuracy of LUR estimates by increasing regression errors and uncertainty; and statistical methods for resolving these effects--e.g., spatially autoregressive (SAR) and geographically weighted regression (GWR) models--may be difficult to apply simultaneously. We used an alternate approach to address spatial non-stationarity and spatial autocorrelation in LUR models for nitrogen dioxide. Traditional models were re-specified to include a variable capturing wind speed and direction, and re-fit as GWR models. Mean R(2) values for the resulting GWR-wind models (summer: 0.86, winter: 0.73) showed a 10-20% improvement over traditional LUR models. GWR-wind models effectively addressed both spatial effects and produced meaningful predictive models. These results suggest a useful method for improving spatially explicit models.

Spatial and temporal epidemiological analysis in the Big Data era

Concurrent with global economic development in the last 50 years, the opportunities for the spread of existing diseases and emergence of new infectious pathogens, have increased substantially. The activities associated with the enormously intensified global connectivity have resulted in large amounts of data being generated, which in turn provides opportunities for generating knowledge that will allow more effective management of animal and human health risks. This so-called Big Data has, more recently, been accompanied by the Internet of Things which highlights the increasing presence of a wide range of sensors, interconnected via the Internet. Analysis of this data needs to exploit its complexity, accommodate variation in data quality and should take advantage of its spatial and temporal dimensions, where available. Apart from the development of hardware technologies and networking/communication infrastructure, it is necessary to develop appropriate data management tools that make this data accessible for analysis. This includes relational databases, geographical information systems and most recently, cloud-based data storage such as Hadoop distributed file systems. While the development in analytical methodologies has not quite caught up with the data deluge, important advances have been made in a number of areas, including spatial and temporal data analysis where the spectrum of analytical methods ranges from visualisation and exploratory analysis, to modelling. While there used to be a primary focus on statistical science in terms of methodological development for data analysis, the newly emerged discipline of data science is a reflection of the challenges presented by the need to integrate diverse data sources and exploit them using novel data- and knowledge-driven modelling methods while simultaneously recognising the value of quantitative as well as qualitative analytical approaches. Machine learning regression methods, which are more robust and can handle large datasets faster than classical regression approaches, are now also used to analyse spatial and spatio-temporal data. Multi-criteria decision analysis methods have gained greater acceptance, due in part, to the need to increasingly combine data from diverse sources including published scientific information and expert opinion in an attempt to fill important knowledge gaps. The opportunities for more effective prevention, detection and control of animal health threats arising from these developments are immense, but not without risks given the different types, and much higher frequency, of biases associated with these data.

Deprivation and suicide mortality across 424 neighborhoods in Seoul, South Korea: a Bayesian spatial analysis

OBJECTIVES

A neighborhood-level analysis of mortality from suicide would be informative in developing targeted approaches to reducing suicide. This study aims to examine the association of community characteristics with suicide in the 424 neighborhoods of Seoul, South Korea.

METHODS

Neighborhood-level mortality and population data (2005-2011) were obtained to calculate age-standardized suicide rates. Eight community characteristics and their associated deprivation index were employed as determinants of suicide rates. The Bayesian hierarchical model with mixed effects for neighborhoods was used to fit age-standardized suicide rates and other covariates with consideration of spatial correlations.

RESULTS

Suicide rates for 424 neighborhoods were between 7.32 and 71.09 per 100,000. Ninety-nine percent of 424 neighborhoods recorded greater suicide rates than the Organization for Economic Cooperation and Development member countries' average. A stepwise relationship between area deprivation and suicide was found. Neighborhood-level indicators for lack of social support (residents living alone and the divorced or separated) and socioeconomic disadvantages (low educational attainment) were positively associated with suicide mortality after controlling for other covariates.

CONCLUSIONS

Finding from this study could be used to identify priority areas and to develop community-based programs for preventing suicide in Seoul, South Korea.

Predictors, spatial distribution, and occurrence of woody invasive plants in subtropical urban ecosystems

We examined the spatial distribution, occurrence, and socioecological predictors of woody invasive plants (WIP) in two subtropical, coastal urban ecosystems: San Juan, Puerto Rico and Miami-Dade, United States. These two cities have similar climates and ecosystems typical of subtropical regions but differ in socioeconomics, topography, and urbanization processes. Using permanent plot data, available forest inventory protocols and statistical analyses of geographic and socioeconomic spatial predictors, we found that landscape level distribution and occurrence of WIPs was not clustered. We also characterized WIP composition and occurrence using logistic models, and found they were strongly related to the proportional area of residential land uses. However, the magnitude and trend of increase depended on median household income and grass cover. In San Juan, WIP occurrence was higher in areas of high residential cover when incomes were low or grass cover was low, whereas the opposite was true in Miami-Dade. Although Miami-Dade had greater invasive shrub cover and numbers of WIP species, San Juan had far greater invasive tree density, basal area and crown cover. This study provides an approach for incorporating field and available census data in geospatial distribution models of WIPs in cities throughout the globe. Findings indicate that identifying spatial predictors of WIPs depends on site-specific factors and the ecological scale of the predictor. Thus, mapping protocols and policies to eradicate urban WIPs should target indicators of a relevant scale specific to the area of interest for their improved and proactive management.

The potential of canine sentinels for reemerging Trypanosoma cruzi transmission

BACKGROUND

Chagas disease, a vector-borne disease transmitted by triatomine bugs and caused by the parasite Trypanosoma cruzi, affects millions of people in the Americas. In Arequipa, Peru, indoor residual insecticide spraying campaigns are routinely conducted to eliminate Triatoma infestans, the only vector in this area. Following insecticide spraying, there is risk of vector return and reinitiation of parasite transmission. Dogs are important reservoirs of T. cruzi and may play a role in reinitiating transmission in previously sprayed areas. Dogs may also serve as indicators of reemerging transmission.

METHODS

We conducted a cross-sectional serological screening to detect T. cruzi antibodies in dogs, in conjunction with an entomological vector collection survey at the household level, in a disease endemic area that had been treated with insecticide 13 years prior. Spatial clustering of infected animals and vectors was assessed using Ripley's K statistic, and the odds of being seropositive for dogs proximate to infected colonies was estimated with multivariate logistic regression.

RESULTS

There were 106 triatomine-infested houses (41.1%), and 45 houses infested with T. cruzi-infected triatomine insects (17.4%). Canine seroprevalence in the area was 12.3% (n=154); all seropositive dogs were 9 months old or older. We observed clustering of vectors carrying the parasite, but no clustering of seropositive dogs. The age- and sex-adjusted odds ratio between seropositivity to T. cruzi and proximity to an infected triatomine (≤50m) was 5.67 (95% CI: 1.12-28.74; p=0.036).

CONCLUSIONS

Targeted control of reemerging transmission can be achieved by improved understanding of T. cruzi in canine populations. Our results suggest that dogs may be useful sentinels to detect re-initiation of transmission following insecticide treatment. Integration of canine T. cruzi blood sampling into existing interventions for zoonotic disease control (e.g., rabies vaccination programs) can be an effective method of increasing surveillance and improving understanding of disease distribution.

Bioassessment of trace element contamination of Mediterranean coastal waters using the seagrass Posidonia oceanica

A large scale survey of the trace element (TE) contamination of Mediterranean coastal waters was performed from the analysis of Ag, As, Cd, Cu, Hg, Ni and Pb in the bioindicator Posidonia oceanica, sampled at 110 sites differing by their levels of exposure to contaminants. The holistic approach developed in this study, based on the combined utilization of several complementary monitoring tools, i.e. water quality scale, pollution index and spatial analysis, accurately assessed the TE contamination rate of Mediterranean coastal waters. In particular, the mapping of the TE contamination according to a new proposed 5-level water quality scale precisely outlined the contamination severity along Mediterranean coasts and facilitated interregional comparisons. Finally, the reliability of the use of P. oceanica as bioindicator species was again demonstrated through several global, regional and local detailed case studies. NB: The designations employed and the presentation of the information in this document do not imply the expression of any opinion whatsoever on the part of the authors concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.

Geospatial patterns in influenza vaccination: evidence from uninsured and publicly insured children in North Carolina

BACKGROUND

The purpose of this study was to explore geospatial patterns in influenza vaccination.

METHODS

We conducted an ecological analysis of publicly funded influenza vaccinations at the ZIP code tabulation area (ZCTA) level using secondary data for publicly funded influenza vaccinations among eligible school-aged children (age range, 5-17 years) for the 2010-2011 and 2011-2012 influenza seasons from the North Carolina Immunization Registry (NCIR). NCIR data were merged by ZCTA with other publicly available data. We tested for spatial autocorrelation in unadjusted influenza vaccination rates using choropleth maps and Moran's I. We estimated nonspatial and spatial negative binomial models with spatially correlated random effects adjusted for demographic, economic, and health care variables. The study was conducted at the University of North Carolina at Chapel Hill in the spring of 2014.

RESULTS

The NCIR demonstrated spatial autocorrelation in publicly funded influenza vaccinations among uninsured and means-tested, publicly insured school-aged children; ZCTAs tended to have influenza vaccination rates that were similar to their neighbors. This result was partially explained by included ZCTA characteristics, but not wholly.

CONCLUSION

To the extent that the geospatial clustering of vaccination rates is the result of social influences, targeting interventions to increase influenza vaccination among school-aged children in one area could also lead to increases in neighboring areas.

Comparing methods of measuring geographic patterns in temporal trends: an application to county-level heart disease mortality in the United States, 1973 to 2010

PURPOSE

To demonstrate the implications of choosing analytical methods for quantifying spatiotemporal trends, we compare the assumptions, implementation, and outcomes of popular methods using county-level heart disease mortality in the United States between 1973 and 2010.

METHODS

We applied four regression-based approaches (joinpoint regression, both aspatial and spatial generalized linear mixed models, and Bayesian space-time model) and compared resulting inferences for geographic patterns of local estimates of annual percent change and associated uncertainty.

RESULTS

The average local percent change in heart disease mortality from each method was -4.5%, with the Bayesian model having the smallest range of values. The associated uncertainty in percent change differed markedly across the methods, with the Bayesian space-time model producing the narrowest range of variance (0.0-0.8). The geographic pattern of percent change was consistent across methods with smaller declines in the South Central United States and larger declines in the Northeast and Midwest. However, the geographic patterns of uncertainty differed markedly between methods.

CONCLUSIONS

The similarity of results, including geographic patterns, for magnitude of percent change across these methods validates the underlying spatial pattern of declines in heart disease mortality. However, marked differences in degree of uncertainty indicate that Bayesian modeling offers substantially more precise estimates.

"Geography of suicide in Hong Kong: spatial patterning, and socioeconomic correlates and inequalities"

Past urban research on Western nations tends to show high suicide rates in inner city and socioeconomically deprived areas. However, little is known about geographic variations in suicide in non-Western cities. We used Bayesian hierarchical models to estimate smoothed standardised mortality ratios (2005-2010) for suicide in people aged 10 years or above in each geographic unit in Hong Kong at two levels, i.e. large street block (n = 1639; median population = 1860) and small tertiary planning unit group (n = 204; median population = 14,850). We further analysed their associations with a range of area socioeconomic characteristics and a deprivation index. The "city centre" of Hong Kong, a generally non-deprived area, showed mostly below average suicide rates. However, there were high rates concentrating in some socioeconomically deprived, densely populated areas, including some inner city areas, across the city. Males had greater geographic variations in rates than females, except the elderly group. The use of smaller geographic units revealed finer detailed suicide distribution than the use of larger units, and showed that suicide rates were associated with indicators of socioeconomic deprivation (population with non-professional jobs and low median household income), and social fragmentation (proportions of unmarried adults and divorced/separated adults), but not with Gini coefficient. Sex/age groups had different associations with suicide rates. Areas in the most deprived quintile had a suicide rate more than two times higher than the least deprived. The association between suicide and deprivation was stronger in males than females and more marked in the younger populations compared to the elderly. The spatial distribution of suicide in Hong Kong showed distinct patterning and a stronger association with income compared to findings from Western countries. Suicide prevention strategies should consider tackling the marked socioeconomic gradient in suicide and high risk in young and middle-aged males living in deprived areas.

Assessment of the relationship between entomologic indicators of Aedes aegypti and the epidemic occurrence of dengue virus 3 in a susceptible population, São José do Rio Preto, São Paulo, Brazil

The aims of this study were to describe the occurrence of dengue in space and time and to assess the relationships between dengue incidence and entomologic indicators. We selected the dengue autochthonous cases that occurred between September 2005 and August 2007 in São José do Rio Preto to calculate incidence rates by month, year and census tracts. The monthly incidence rates of the city were compared to the monthly Breteau indices (BI) of the São José do Rio Region. Between December 2006 and February 2007, an entomological survey was conducted to collect immature forms of Aedes aegypti in Jaguaré, a São José do Rio Preto neighborhood, and to obtain entomological indices. These indices were represented using statistical interpolation. To represent the occurrence of dengue in the Jaguaré neighborhood in 2006 and 2007, we used the Kernel ratio and to evaluate the relationship between dengue and the entomological indices, we used a generalized additive model in a spatial case-control design. Between September 2005 and August 2007, the occurrence of dengue in São José do Rio Preto was almost entirely caused by DENV3, and the monthly incidence rates presented high correlation coefficients with the monthly BI. In Jaguaré neighborhood, the entomological indices calculated by hectare were better predictors of the spatial distribution of dengue than the indices calculated by properties, but the pupae quantification did not show better prediction qualities than the indices based on the container positivity, in relation to the risk of dengue occurrence. The fact that the municipality's population had a high susceptibility to the serotype DENV3 before the development of this research, along with the almost total predominance of the occurrence of this serotype between 2005 and 2007, facilitated the analysis of the epidemiological situation of the disease and allowed us to connect it to the entomological indicators.

Stress changes the spatial arrangement of neurons and glial cells of medial prefrontal cortex and sertraline and curcumin prevent it

OBJECTIVE

The present study explored the three-dimensional spatial arrangements of the neurons and glial cells within the medial prefrontal cortex (mPFC) of rats.

METHODS

It evaluated the arrangement for differences after stress with or without treatment with curcumin and sertraline using second-order stereology. Orientator method was applied to obtain isotropic uniform random sections of mPFC. The pair correlation g(r) and cross-correlation functions were estimated by counting dipole probes superimposed on histological sections of mPFC.

RESULTS

The mean total volume of neurons and glial cells was 0.80 (0.05) and 0.40 (0.07), respectively in the control group. The corresponding values decreased by 50% in the stressed group. The curve of g(r) for the neurons and glial cells showed a wider gap between the stressed rats' mPFC. Theses indicate a negative correlation (repulsion) between the neurons and glial cells in the stressed rats. Evaluation of the cross-correlation function of the neurons and glial cells also showed a negative correlation in the stressed group. The estimated values of the global degree of order in the spatial point pattern for neurons and glial cells were 0.62 and 0.20 in control and stressed animals, respectively. Curcumin and sertraline protected the spatial arrangements of the cells after stress induction in rats. In addition, the volume of the neurons and glial cells remained unchanged after stress.

CONCLUSION

Dissociation of the neurons and glial cells can is seen at some places in the stressed rats' cortex. However, the spatial arrangement of the cells was remained unchanged in curcumin+stress and sertraline+stress rats.

Spatial analysis of gastroschisis in Massachusetts and Texas

PURPOSE

Previous research has suggested gastroschisis, a congenital malformation, may be linked to environmental or infectious factors and cases can occur in clusters. The objective of this study was to identify geographic areas of elevated gastroschisis risk.

METHODS

Cases of gastroschisis were identified from birth defect registries in Massachusetts and Texas. Random samples of live births were selected as controls. Generalized additive models were used to create a continuous map surface of odds ratios (ORs) by smoothing over latitude and longitude. Maternal age, race/ethnicity, education, cigarette smoking, and insurance status (MA only) were assessed for confounding. We used permutation tests to identify statistically significant areas of increased risk.

RESULTS

An area of increased risk was identified in North Central Massachusetts but was not significant after adjustment (P value = .07; OR = 2.0). In Texas, two statistically significant areas of increased risk were identified after adjustment (P value = .02; OR = 1.3 and 1.2). Texas had sufficient data to assess the combination of space and time, which identified an increased risk in 2003 and 2004.

CONCLUSIONS

This study suggests there were areas of elevated gastroschisis risk in Massachusetts and Texas, which cannot be explained by the risk factors we assessed. Additional exploration of underlying artifactual, environmental, infectious, or behavioral factors may further our understanding of gastroschisis.

The spatial extent of the effect of foreclosures on crime

Although neighborhood stability has long been considered a substantial determinant of crime, foreclosures have not been the subject of concerted research among criminologists until recently. A number of recent studies have examined the linkage between home foreclosures and crime. Though generally finding a significant relationship, studies have used different approaches and units of analysis. This variation led us to examine the spatial extent to which foreclosures affect a relatively small surrounding area. In this paper, we consider the spatial extent of the foreclosure effect on crime by estimating fixed effect negative binomial models using geocoded UCR data for 2003-2008 and foreclosure data to predict crime counts using the number of foreclosures within various small area radii. Results show that, independently and jointly, foreclosures are a predictor of crime up to at least a distance of 2250 feet. Importantly, that effect declines with distance. We conclude with a discussion of the implications of those findings.

Spatial analysis of suicide mortality in Québec: spatial clustering and area factor correlates

Understanding the spatial distribution of suicide can inform the planning, implementation and evaluation of suicide prevention actions. No previous study has assessed spatial clustering of the different methods of suicide in Quebec. The aim of this study was to assess spatial clustering of suicide in Quebec between 2004 and 2007 and neighborhood level predictors of the clusters. Scan statistics was applied to detect clusters of suicides by method and by sex. Smoothed standardized mortality ratios (SMRs) for suicide for each neighborhood were also estimated and their association with neighborhood characteristics was investigated using the Bayesian hierarchical spatial model. The pattern of suicide rate was different among men and women; men showed higher standardized mortality rates. The most likely clusters of suicide were found in remote rural areas. However, some neighborhoods in urban areas also had noticeable suicide clusters. Firearms suicide was most likely found in remote rural areas while poisoning and hanging suicide methods clustered in urban areas. These findings suggest that it is important to take geographical variations into account in national policy and health services planning.

Analysis of biophysical and anthropogenic variables and their relation to the regional spatial variation of aboveground biomass illustrated for North and East Kalimantan, Borneo

BACKGROUND

Land use and land cover change occurring in tropical forest landscapes contributes substantially to carbon emissions. Better insights into the spatial variation of aboveground biomass is therefore needed. By means of multiple statistical tests, including geographically weighted regression, we analysed the effects of eight variables on the regional spatial variation of aboveground biomass. North and East Kalimantan were selected as the case study region; the third largest carbon emitting Indonesian provinces.

RESULTS

Strong positive relationships were found between aboveground biomass and the tested variables; altitude, slope, land allocation zoning, soil type, and distance to the nearest fire, road, river and city. Furthermore, the results suggest that the regional spatial variation of aboveground biomass can be largely attributed to altitude, distance to nearest fire and land allocation zoning.

CONCLUSIONS

Our study showed that in this landscape, aboveground biomass could not be explained by one single variable; the variables were interrelated, with altitude as the dominant variable. Spatial analyses should therefore integrate a variety of biophysical and anthropogenic variables to provide a better understanding of spatial variation in aboveground biomass. Efforts to minimise carbon emissions should incorporate the identified factors, by 1) the maintenance of lands with high AGB or carbon stocks, namely in the identified zones at the higher altitudes; and 2) regeneration or sustainable utilisation of lands with low AGB or carbon stocks, dependent on the regeneration capacity of the vegetation. Low aboveground biomass densities can be found in the lowlands in burned areas, and in non-forest zones and production forests.

A geographical comparison of chlorinated, brominated and fluorinated compounds in seabirds breeding in the eastern Canadian Arctic

A suite of chlorinated, brominated and fluorinated organic contaminants were measured in livers of adult thick-billed murres (Uria lomvia) and northern fulmars (Fulmarus glacialis) from several locations in the eastern Canadian Arctic during 2007-2008. Thick-billed murres were collected from five colonies (Coats Island, Digges Island, Akpatok Island, Prince Leopold Island, Minarets) and northern fulmars from two colonies (Prince Leopold Island, Minarets). Legacy organochlorines (e.g. PCBs, DDT, chlorobenzenes, chlordanes) and perfluorooctane sulfonate (PFOS) dominated the compositional profiles of the measured halogenated compounds in the livers of both species at all colonies. Among the murre colonies sampled, Prince Leopold Island birds generally had the highest mean concentrations of organochlorines, whereas the highest mean concentration of sum (Σ) polybrominated diphenyl ethers (PBDEs) was found at the Minarets and the lowest at Prince Leopold Island. PBDEs were detected in only a few fulmar livers from the Minarets and in none of the fulmar livers from Prince Leopold Island. Mean PFOS concentrations were highest in both murre and fulmar livers at Prince Leopold Island. PFOS was approximately two orders of magnitude higher than the mean sum (Σ) perfluorinated carboxylate (PFCA) concentration in both species and at all colonies. The reasons for inter-colony and inter-species differences in contaminant liver levels are probably variable and complex, and likely reflect differences in contaminant transport and exposure pathways, as well as differences among colonies in their diets and overwintering areas. To our knowledge, this is the first spatial assessment of PBDEs, PFCAs and PFOS in seabirds from the Canadian Arctic.

Analysis of nodal point pollution, variability, and sustainability in mesohaline tidal creeks

Mesohaline tidal creeks are critical since they may lie at the crossroads of aquatic habitat and urban/sub-urban pressures. The emphasis of this study was to determine the water quality stressor variations within and between tidal creeks and determine whether they serve as nodes of pollutants into the sub-estuary. Measurements of water quality stressors were conducted over a six-year period. The study revealed that characterizing the variability of individual tidal creeks is critical to understanding the process and impacts of stressors in sub-estuarine environments and that the tidal creeks are actually nodal points of sediment and nutrient pollution. This results in hypoxia being controlled within tidal creeks rather than being imported from the parent estuary. The calculated metrics were then used to create a Sustainability Characterization Map. Methods incorporated in this study would be of value to restoration managers, and in the decision-making process of urban and suburban watershed planners.

Exploring the impact of climate variability during the Last Glacial Maximum on the pattern of human occupation of Iberia

The Last Glacial Maximum (LGM) was a global climate event, which had significant repercussions for the spatial distribution and demographic history of prehistoric populations. In Eurasia, the LGM coincides with a potential bottleneck for modern humans and may mark the divergence date for Asian and European populations (Keinan et al., 2007). In this research, the impact of climate variability on human populations in the Iberian Peninsula during the Last Glacial Maximum (LGM) is examined with the aid of downscaled high-resolution (16 × 16 km) numerical climate experiments. Human sensitivity to short time-scale (inter-annual) climate variability during this key time period, which follows the initial modern human colonisation of Eurasia and the extinction of the Neanderthals, is tested using the spatial distribution of archaeological sites. Results indicate that anatomically modern human populations responded to small-scale spatial patterning in climate variability, specifically inter-annual variability in precipitation levels as measured by the standard precipitation index. Climate variability at less than millennial scale, therefore, is shown to be an important component of ecological risk, one that played a role in regulating the spatial behaviour of prehistoric human populations and consequently affected their social networks.