Spatial Risk Model Research Articles

Spatial risk profiling of Plasmodium falciparum parasitaemia in a high endemicity area in Côte d'Ivoire

BackgroundThe objective of this study was to identify demographic, environmental and socioeconomic risk factors and spatial patterns of Plasmodium falciparum parasitaemia in a high endemicity area of Africa, and to specify how this information can facilitate improved malaria control at the district level.MethodsA questionnaire was administered to about 4,000 schoolchildren in 55 schools in western Côte d'Ivoire to determine children's socioeconomic status and their habit of sleeping under bed nets. Environmental data were obtained from satellite images, digitized ground maps and a second questionnaire addressed to school directors. Finger prick blood samples were collected and P. falciparum parasitaemia determined under a microscope using standardized, quality-controlled methods. Bayesian variogram models were utilized for spatial risk modelling and mapping of P. falciparum parasitaemia at non-sampled locations, assuming stationary and non-stationary underlying spatial dependence.ResultsTwo-thirds of the schoolchildren were infected with P. falciparum and the mean parasitaemia among infected children was 959 parasites/μl of blood. Age, socioeconomic status, not sleeping under a bed net, coverage rate with bed nets and environmental factors (e.g., normalized difference vegetation index, rainfall, land surface temperature and living in close proximity to standing water) were significantly associated with the risk of P. falciparum parasitaemia. After accounting for spatial correlation, age, bed net coverage, rainfall during the main malaria transmission season and distance to rivers remained significant covariates.ConclusionIt is argued that a massive increase in bed net coverage, particularly in villages in close proximity to rivers, in concert with other control measures, is necessary to bring malaria endemicity down to intermediate or low levels.

A GIS-based human health risk assessment for urban green space planning—An example from Grugliasco (Italy)

The need to develop approaches for risk-based management of soil contamination, as well as the integration of the assessment of the human health risk (HHR) due to the soil contamination in the urban planning procedures has been the subject of recent attention of scientific literature and policy makers. The spatial analysis of environmental data offers multiple advantages for studying soil contamination and HHR assessment, facilitating the decision making process. The aim of this study was to explore the possibilities and benefits of spatial implementation of a quantitative HHR assessment methodology for a planning case in a typical urban environment where the soil is contaminated. The study area is located in the city of Grugliasco a part of the Turin (Italy) metropolitan area. The soils data were derived from a site specific soil survey and the land-use data from secondary sources. In the first step the soil contamination data were geo-statistically analysed and a spatial soil contamination data risk modelling procedure designed. In order to spatially assess the HHR computer routines were developed using GIS raster tools. The risk was evaluated for several different land uses for the planned naturalistic park area. The HHR assessment indicated that the contamination of soils with heavy metals in the area is not sufficient to induce considerable health problems due to typical human behaviour within the variety of urban land uses. An exception is the possibility of direct ingestion of contaminated soil which commonly occurs in playgrounds. The HHR evaluation in a planning case in the Grugliasco Municipality confirms the suitability of the selected planning option. The construction of the naturalistic park presents one solution for reducing the impacts of soil contamination on the health of citizens. The spatial HHR evaluation using GIS techniques is a diagnostic procedure for assessing the impacts of urban soil contamination, with which one can verify planning options, and provides an important step in the integration of human health protection within urban planning procedures.

Spatial Risk Models for Human Plague in the West Nile Region of Uganda

The West Nile region of Uganda represents an epidemiologic focus for human plague in east Africa. However, limited capacity for diagnostic laboratory testing means few clinically diagnosed cases are confirmed and the true burden of disease is undetermined. The aims of the study were 1) describe the spatial distribution of clinical plague cases in the region, 2) identify ecologic correlates of incidence, and 3) incorporate these variables into predictive models that define areas of plague risk. The model explained 74% of the incidence variation and revealed that cases were more common above 1,300 m than below. Remotely-sensed variables associated with differences in soil or vegetation were also identified as incidence predictors. The study demonstrated that plague incidence can be modeled at parish-level scale based on environmental variables and identified parishes where cases may be under-reported and enhanced surveillance and preventative measures may be implemented to decrease the burden of plague.

Improving Methods for Reporting Spatial Epidemiologic Data

In his comment, Peterson reiterates the need for improved methods for collecting and presenting spatial epidemiologic data for vector-borne diseases (1). He agrees with us that lack of reliable data on probable pathogen exposure site is an obstacle to the development of predictive spatial risk models (2). In that article we noted, “New methods are urgently needed to determine probable pathogen exposure sites that will yield reliable results while taking into account economic and time constraints of the public health system and attending physicians.” Peterson suggests that the point-radius method is a viable solution to this problem. Unfortunately, its practical implementation for vector-borne diseases is neither reliable nor cost-effective. With regard to practical implementation of the point-radius method in a public health setting, Peterson states, “If data are to be captured initially on paper, the data recorder would simply record the focal point of the person’s activities (usually a residence) and an approximate description of the person’s movements (e.g., broadly across the state, housebound, within 20 miles)” (1). We find a number of serious problems with this approach to determining probable sites of pathogen exposure, primarily that meaningful use of the point-radius method 1) will require not only recording detailed movements during the perceived window of opportunity for pathogen exposure but also weighting of risk by activity type and, for some vector-borne diseases, time of day; and 2) will require the public health community to allocate resources to in-depth interviews conducted by specially trained personnel. Our first concern is that Peterson’s scenario does not distinguish between a car trip to the mall at noon and spending an evening on the golf course. In reality, one activity presents minimal risk for exposure to mosquitoes infected with West Nile virus, whereas the other is a potential high-risk activity. Giving equal weight to the movements represented by these activities will assuredly produce an unreliable result for probable pathogen exposure site. Other issues are patient recall and reluctance to provide information on movement patterns and specific activities. Peterson’s suggestion that the data recorder would simply record the focal point of the person’s activities and an approximate description of the person’s movements is therefore a grossly oversimplified solution to a complex public health problem. With regard to the second concern, the average physician likely lacks the knowledge, time, and training in vector-borne disease epidemiology and ecology needed to accurately assess when and where risk for pathogen exposure occurred. To be of use, the method will require in-depth patient interviews by specially trained personnel from local or state health departments. Even then, we doubt that the quality of data gleaned would justify the cost incurred. We fail to see that the quality of information gathered by using the point-radius method would be an improvement over our suggestion. In our original article, we suggested using sets of standardized questions that are tailored to a given vector-borne disease. We also indicated that a critical minimal need includes a basic assessment of whether pathogen exposure likely occurred in 1) the peridomestic environment, 2) outside the peridomestic environment but within the county of residence, or 3) outside the county of residence (2). The challenge of how to most effectively collect and present spatial epidemiologic data is neither conceptual nor technologic; rather, it is logistic and legal. Any new method must 1) weigh the public health utility of the method against the time and cost required for the public health system to implement it and 2) comply with existing patient privacy laws. The point-radius method clearly fails on the first count and also likely will present substantial problems in terms of patient privacy. We agree that presenting data for case counts and disease incidence by ZIP code or census tract falls short of the desired level of spatial precision. However, this realistic compromise 1) is a marked improvement over the current practice to display only county-based spatial patterns for case counts or incidence; 2) incurs only minimal added time and cost for the public health community; and 3) can be implemented, especially for census tracts, with minimal concerns regarding patient privacy.

Spatial Modeling of Human Risk of Exposure to Vector-Borne Pathogens Based on Epidemiological Versus Arthropod Vector Data

Understanding spatial patterns of human risk of exposure to arthropod vectors and their associated pathogens is critical for targeting limited prevention, surveillance, and control resources (e.g., spatial targeting of vaccination, drug administration, or education campaigns; use of sentinel sites to monitor vector abundance; and identifying areas for most effective use of pesticides). Vector-borne disease risk can, in many cases, be modeled with high predictive accuracy by using geographic information system approaches because abundances of vectors and pathogen reservoirs often are associated with environmental factors. Spatial risk models for human exposure to vector-borne pathogens, which ideally should have high accuracy for predicting areas of elevated risk without overestimating risk coverage, can be constructed based on epidemiological data or abundance of vectors or infected vectors. We use five bacterial or viral vector-borne diseases occurring in the United States and with pathogen transmission by fleas (plague), ticks (Lyme disease and tularemia), or mosquitoes (dengue and West Nile virus disease) to 1) examine how spatial risk of human exposure to vector-borne pathogens typically is presented to the public health community and public and 2) evaluate the utility of basing spatial risk models on epidemiological data relative to data for arthropod vectors or infected vectors. Recommended future directions for vector-borne disease risk modeling include development of subcounty level spatial risk models combining epidemiological and vector data and the use of simulation or analytical models to assess critical vector abundance thresholds required for enzootic pathogen maintenance.

Need for Improved Methods to Collect and Present Spatial Epidemiologic Data for Vectorborne Diseases

Improved methods for collection and presentation of spatial epidemiologic data are needed for vectorborne diseases in the United States. Lack of reliable data for probable pathogen exposure site has emerged as a major obstacle to the development of predictive spatial risk models. Although plague case investigations can serve as a model for how to ideally generate needed information, this comprehensive approach is cost-prohibitive for more common and less severe diseases. New methods are urgently needed to determine probable pathogen exposure sites that will yield reliable results while taking into account economic and time constraints of the public health system and attending physicians. Recent data demonstrate the need for a change from use of the county spatial unit for presentation of incidence of vectorborne diseases to more precise ZIP code or census tract scales. Such fine-scale spatial risk patterns can be communicated to the public and medical community through Web-mapping approaches.

Spatial energy market risk analysis using the semivariance risk measure

The paper concentrates on the analysis of semivariance (SV) as a market risk measure for market risk analysis of mean–semivariance (MSV) portfolios. The advantage of MSV over variance as a risk measure is that MSV provides a more logical measure of risk than the MV method. In addition, the relationship of the SV with the lower partial movements is discussed. A spatial risk model is proposed as a basis of risk assessment for short-term energy markets. Transaction costs and other practical constraints are also included. A case study is provided to show the successful application of the model.

Modeling the risk for a new invasive forest disease in the United States: An evaluation of five environmental niche models

Efforts to model the potential habitat and risk for spread of invasive diseases such as Sudden Oak Death (SOD) are important for disease regulation and management. However, spatially referenced risk models using identical data can have differing results, making decision-making based on the mapped results problematic. We examined the results from five spatial risk models generated from common input parameters, and investigated model agreement for mapping risk for the causal pathogen for SOD, Phytophthora ramorum across the conterminous United States. We examined five models: Expert-driven Rule-based, Logistic Regression, Classification and Regression Trees, Genetic Algorithms, and Support Vector Machines. All models were consistent in their prediction of some SOD risk in coastal California, Oregon and Washington states, and in the northern foothills of the Sierra Nevada Mountains in California, and in an east–west oriented band including eastern Oklahoma, central Arkansas, Tennessee, Kentucky, northern Mississippi, Alabama, Georgia and South Carolina, parts of central North Carolina, and eastern Virginia, Delaware and Maryland states. The SVM model was the most accurate model, and had several advantages over the other models. Although theoretical in nature, this paper presents results that have practical, applied value for managers and regulators of this disease, and discusses common challenges in modeling invasive species niches over large scales.

Spatially Modelling and Ranking the Risks of Helicobacter Pylori Infection

P-777 Introduction: The Helicobacter pylori infection (H. pylori), a disease of the gastrointestinal tract, influences health and well being of people. Even though this disease is widespread, infection routes of H. pylori are not completely detected. Hygienic conditions play a central role for the infection pathway, but there are also environmental and socio-economic risk factors. There are spatial differences of the disease occurrence within a geographical area, possibly caused by spatial heterogeneity in risk factors and the spatial differences in population distribution. Spatial risk assessment, considering the heterogeneity in risk factors as well as the spatial correlation between the data, allows studying the relationship of those spatial differences. Methods: We used epidemiological data for H. pylori from a cross sectional study in Leipzig and 2 rural districts, Germany, including socio-economic and also hygienic risk factors. We applied conditional autoregressive models (CAR), which take into account spatial heterogeneity of risk factors and the population distribution and, additionally, consider spatial correlation between neighbored or nearby data. This method splits the total spatial disease risk into (1) exposure related risk (exposure risk) and (2) risk related to spatial locations of the sub areas (area-specific location risk). Results: Exposure to poor hygienic conditions, to environmental tobacco smoke (ETS) and to further factors is associated with increased risk of H. pylori infection in children. We ranked all risk factors by the intensity of their health effect, accounting for correlation between the risk factors. Before adjusting the spatial disease risks for important risk factors, we observed several hot spots for the H. pylori infection over the area of interest. After taking the heterogeneities in exposure distribution into account, only some of the previously identified hot spots of the area-specific location risk remained statistically significant. Conclusions: Spatial differences in risk may be caused by a heterogeneous distribution of risk factors. The presented CAR model splits the total risk of infection into risks attributable to several risk factors and offers extensive possibilities to estimate unbiased spatial risks by taking into account the specific characteristics of spatial data. Aknowledgements: This research was financially supported by the European Union (European Commission, FP6 Contract No. 003956). Key Words: risk ranking, CAR, conditional autoregressive model, spatial correlation, spatial risk modeling

Spatiotemporal Patterns of Host-Seeking Ixodes scapularis Nymphs (Acari: Ixodidae) in the United States

Abstract The risk of Lyme disease for humans in the eastern United States is dependent on the density of host-seeking Ixodes scapularis Say nymphal stage ticks infected with Borrelia burgdorferi. Although many local and regional studies have estimated Lyme disease risk using these parameters, this is the first large-scale study using a standardized methodology. Density of host-seeking I. scapularis nymphs was measured by drag sampling of closed canopy deciduous forest habitats in 95 locations spaced among 2° quadrants covering the entire United States east of the 100th meridian. Sampling was done in five standardized transects at each site and repeated three to six times during the summer of 2004. The total number of adults and nymphs of the seven tick species collected was 17,972, with 1,405 nymphal I. scapularis collected in 31 of the 95 sites. Peak global spatial autocorrelation values were found at the smallest lag distance (300 km) and decreased significantly after 1,000 km. Local autocorrelation statistics identified two significant high-density clusters around endemic areas in the northeast and upper Midwest and a low-density cluster in sites south of the 39th parallel, where only 21 nymphs were collected. Peak nymphal host-seeking density occurred earlier in the southern than in the most northern sites. Spatiotemporal density patterns will be combined with Borrelia prevalence data as part of a 4-yr survey to generate a nationwide spatial risk model for I. scapularis-borne Borrelia, which will improve targeting of disease prevention efforts.

Spatiotemporal Patterns of Host-Seeking <I>Ixodes scapularis</I> Nymphs (Acari: Ixodidae) in the United States

The risk of Lyme disease for humans in the eastern United States is dependent on the density of host-seeking Ixodes scapularis Say nymphal stage ticks infected with Borrelia burgdorferi. Although many local and regional studies have estimated Lyme disease risk using these parameters, this is the first large-scale study using a standardized methodology. Density of host-seeking I. scapularis nymphs was measured by drag sampling of closed canopy deciduous forest habitats in 95 locations spaced among 2 degrees quadrants covering the entire United States east of the 100th meridian. Sampling was done in five standardized transects at each site and repeated three to six times during the summer of 2004. The total number of adults and nymphs of the seven tick species collected was 17,972, with 1,405 nymphal I. scapularis collected in 31 of the 95 sites. Peak global spatial autocorrelation values were found at the smallest lag distance (300 km) and decreased significantly after 1,000 km. Local auto-correlation statistics identified two significant high-density clusters around endemic areas in the northeast and upper Midwest and a low-density cluster in sites south of the 39th parallel, where only 21 nymphs were collected. Peak nymphal host-seeking density occurred earlier in the southern than in the most northern sites. Spatiotemporal density patterns will be combined with Borrelia prevalence data as part of a 4-yr survey to generate a nationwide spatial risk model for I. scapularis-borne Borrelia, which will improve targeting of disease prevention efforts.

Spatial risk prediction and mapping of Schistosoma mansoni infections among schoolchildren living in western Côte d'Ivoire

The objectives of this study were (1) to examine risk factors for Schistosoma mansoni infection among schoolchildren living in western Côte d'Ivoire, and (2) to carry forward spatial risk prediction and mapping at non-sampled locations. First, demographic and socio-economic data were obtained from 3818 children, aged 6-16 years, from 55 schools. Second, a single stool sample was examined from each child by the Kato-Katz technique to assess infection status of S. mansoni and its intensity. Third, remotely sensed environmental data were derived from satellite imagery and digitized ground maps. With these databases a comprehensive geographical information system was established. Bayesian variogram models were applied for spatial risk modelling and prediction. The infection prevalence of S. mansoni was 38.9%, ranging from 0% to 89.3% among schools. Results showed that age, sex, the richest wealth quintile, elevation and rainfall explained the geographical variation of the school prevalences of S. mansoni infection. The goodness of fit of different spatial models revealed that age, sex and socio-economic status had a stronger influence on infection prevalence than environmental covariates. The generated risk map can be used by decision-makers for the design and implementation of schistosomiasis control in this setting. If successfully validated elsewhere, this approach can guide control programmes quite generally.

The spatial epidemiology of cholera in an endemic area of Bangladesh

This paper defines high-risk areas of cholera based on environmental risk factors of the disease in an endemic area of Bangladesh. The risk factors include proximity to surface water, high population density, and low educational status, which were identified in an earlier study by the authors. Cholera data were analyzed by spatially referenced extended household units for two time periods, 1983–1987 and 1992–1996. These periods were chosen because they had different dominant cholera agents. From 1983–1987 classical cholera was dominant and from 1992–1996 El Tor was dominant. By defining high-risk areas based on risk factors, this study builds a spatial risk model for cholera. The model is then evaluated based on the locations of observed cholera cases. The study also identifies the determinants of death due to cholera for the two different time periods dominated by the different cholera agents. The modeled risk areas that were based on the risk factors were found to correspond with actual distributions of cholera morbidity and mortality. The high-risk areas of the dominant cholera agents are relatively stable over time. However, from 1983–1987 El Tor cholera, which was not the dominant agent during that period, was not associated with high-risk areas, suggesting that the El Tor habitat may have changed over time. The case fatality rate for cholera was related to proximity to a diarrhea treatment hospital in the study area.

The Behavioral Response Race Between Predator and Prey

On tente de preciser par la methode de regression lineaire ce qui determine les rapports de force relatifs en ce qui concerne les relations predateur-proie (on donne comme exemple Notonectidae-Culicidae)