Improved Geocoding of Cancer Registry Addresses in Urban and Rural Oklahoma

BACKGROUND

Between 1997 and 2013 (the included study years), approximately 23% of addresses in the Oklahoma Central Cancer Registry (OCCR) were not geocoded to the address level. Addresses in rural counties were geocoded with poorer quality, preventing the instructive geographic research that informs policymaking.

METHODS

To improve the accuracy of the geocodes, we first utilized the United States Postal Service's LACSLink database to correct addresses; specifically, to convert old rural route-based addresses to modernized Enhanced 911 (E911) addresses. We created custom geocoders using regional E911 reference data sets and used existing national scope geocoders of NAVTEQ and the North American Association of Central Cancer Registries. We attempted to geocode 5,102 addresses, which are either regular street addresses or rural route addresses. In the process, we evaluated and tabulated performances of the address correction. Accordingly, we first tabulated how well each geocoder could geocode original and LACSLink corrected addresses. We then documented the overall performances of geocoders based on pairwise comparisons.

RESULTS

We were able to geocode 1,945 addresses out of this data set using 5 distinct geocoders. We observed that the LACSLink correction and E911 data were useful in the specific purpose of geocoding rural addresses, as found in the literature.

CONCLUSIONS

We conclude that both LACSLink correction and E911 data were useful for improving geocoding of cancer records, many of which were in rural areas. Future directions include further validation of the geocoding and plans to conduct spatial exploratory data analysis to generate hypotheses related to the distribution of cancer in Oklahoma.

Using spatiotemporal models to generate synthetic data for public use

When agencies release public-use data, they must be cognizant of the potential risk of disclosure associated with making their data publicly available. This issue is particularly pertinent in disease mapping, where small counts pose both inferential challenges and potential disclosure risks. While the small area estimation, disease mapping, and statistical disclosure limitation literatures are individually robust, there have been few intersections between them. Here, we formally propose the use of spatiotemporal data analysis methods to generate synthetic data for public use. Specifically, we analyze ten years of county-level heart disease death counts for multiple age-groups using a Bayesian model that accounts for dependence spatially, temporally, and between age-groups; generating synthetic data from the resulting posterior predictive distribution will preserve these dependencies. After demonstrating the synthetic data's privacy-preserving features, we illustrate their utility by comparing estimates of urban/rural disparities from the synthetic data to those from data with small counts suppressed.

The rationale and cost-effectiveness of a confirmatory mapping tool for lymphatic filariasis: Examples from Ethiopia and Tanzania

Endemicity mapping is required to determining whether a district requires mass drug administration (MDA). Current guidelines for mapping LF require that two sites be selected per district and within each site a convenience sample of 100 adults be tested for antigenemia or microfilaremia. One or more confirmed positive tests in either site is interpreted as an indicator of potential transmission, prompting MDA at the district-level. While this mapping strategy has worked well in high-prevalence settings, imperfect diagnostics and the transmission potential of a single positive adult have raised concerns about the strategy’s use in low-prevalence settings. In response to these limitations, a statistically rigorous confirmatory mapping strategy was designed as a complement to the current strategy when LF endemicity is uncertain. Under the new strategy, schools are selected by either systematic or cluster sampling, depending on population size, and within each selected school, children 9–14 years are sampled systematically. All selected children are tested and the number of positive results is compared against a critical value to determine, with known probabilities of error, whether the average prevalence of LF infection is likely below a threshold of 2%. This confirmatory mapping strategy was applied to 45 districts in Ethiopia and 10 in Tanzania, where initial mapping results were considered uncertain. In 42 Ethiopian districts, and all 10 of the Tanzanian districts, the number of antigenemic children was below the critical cutoff, suggesting that these districts do not require MDA. Only three Ethiopian districts exceeded the critical cutoff of positive results. Whereas the current World Health Organization guidelines would have recommended MDA in all 55 districts, the present results suggest that only three of these districts requires MDA. By avoiding unnecessary MDA in 52 districts, the confirmatory mapping strategy is estimated to have saved a total of $9,293,219.

Mapping is used by lymphatic filariasis (LF) elimination programs to determine if mass drug administration (MDA) is required. The current mapping approach, designed to be simple and practical, has worked well in high-prevalence settings but concerns about its reliability in low-prevalence settings have been raised. To address these concerns, a confirmatory mapping strategy was developed that utilizes probability-based sampling of school attending children to determine if the prevalence of LF antigenemia is below a 2% threshold. The confirmatory mapping strategy was implemented in 45 districts in Ethiopia and 10 in Tanzania where the need for MDA was uncertain. In 52 of the 55 districts, the number of LF antigen-positive children identified by the confirmatory mapping strategy was below the predetermined threshold and MDA was deemed unnecessary, while in three districts the number of positive children exceeded the threshold, suggesting that MDA is required. The use of this mapping strategy, to confirm whether MDA is required, is estimated to have saved the Ethiopian and Tanzanian programs $9,293,219 by avoiding unnecessary MDA in 52 districts.

Retinal topography maps in R: new tools for the analysis and visualization of spatial retinal data

Retinal topography maps are a widely used tool in vision science, neuroscience, and visual ecology, providing an informative visualization of the spatial distribution of cell densities across the retinal hemisphere. Here, we introduce Retina, an R package for computational mapping, inspection of topographic model fits, and generation of average maps. Functions in Retina take cell count data obtained from retinal wholemounts using stereology software. Accurate visualizations and comparisons between different eyes have been difficult in the past, because of deformation and incisions of retinal wholemounts. We account for these issues by incorporation of the R package Retistruct, which results in a retrodeformation of the wholemount into a hemispherical shape, similar to the original eyecup. The maps are generated by thin plate splines, after the data were transformed into a two-dimensional space with an azimuthal equidistant plot projection. Retina users can compute retinal topography maps independent of stereology software choice and assess model fits with a variety of diagnostic plots. Functionality of Retina also includes species average maps, an essential feature for interspecific analyses. The Retina package will facilitate rigorous comparative studies in visual ecology by providing a robust quantitative approach to generate retinal topography maps.

A new approach for cleansing geographical dataset using Levenshtein distance, prior knowledge and contextual information

Epidemiological studies are necessary to take public health decisions. Their relevance depends on the quality of data. Doctors in continuous care collect a big amount of data that can be used for epidemiological purpose, but spatial data may be dirty; based on city names, the localization is imprecise, even more if it is misspelled. The only way to identify a city without ambiguity is to use its identifier, which can be retrieved by cleansing geographical textual data. In France, cities are organized in administrative zones called departments and some city names are shared by several cities in several departments. The clear identification of the department and the city name allows to deduce the city unique identifier and to make some spatial analysis such as epidemiological studies. In this paper, we propose a method to cleanse such data, using several steps. After having standardized the text to cleanse, we use the Levenshtein distance to generate a first set of propositions. Finally, the propositions are filtered, by removing the less likely candidates, so that it remains only one, which becomes the chosen city. Tested on a dataset of 9818 entries, we obtained 89.1% of concordance, whereas the standard Levenshtein distance obtained 70.5%. This demonstrates that our method has better results.

Mapping transmission risk of Lassa fever in West Africa: the importance of quality control, sampling bias, and error weighting

Lassa fever is a disease that has been reported from sites across West Africa; it is caused by an arenavirus that is hosted by the rodent M. natalensis. Although it is confined to West Africa, and has been documented in detail in some well-studied areas, the details of the distribution of risk of Lassa virus infection remain poorly known at the level of the broader region. In this paper, we explored the effects of certainty of diagnosis, oversampling in well-studied region, and error balance on results of mapping exercises. Each of the three factors assessed in this study had clear and consistent influences on model results, overestimating risk in southern, humid zones in West Africa, and underestimating risk in drier and more northern areas. The final, adjusted risk map indicates broad risk areas across much of West Africa. Although risk maps are increasingly easy to develop from disease occurrence data and raster data sets summarizing aspects of environments and landscapes, this process is highly sensitive to issues of data quality, sampling design, and design of analysis, with macrogeographic implications of each of these issues and the potential for misrepresenting real patterns of risk.

Delivering antibiotic resistance information specifically tailored to location and time

Antibiotic resistance poses a significant threat to humanity. Hundred years since the beginning of the era of antibacterial drugs, we are facing increasing numbers of infections with multi-resistant pathogens. The current approach of distributing information on antibiotic resistance in printed form in the clinics has disadvantages with respect to the actuality of the data and the regional heterogeneity of resistance patterns. We developed an application named qRe using representational state transfer as a communication standard to deliver antibiotic resistance percentage information to the end user. The data is selected specifically for his/her geographic location. The user can display the information using either the application for Android smart phones or the web application. With the presented software we show the technical feasibility of delivering antibiotic resistance information specifically tailored to location and time. A short evaluation of the software showed an overall positive response from physicians. Based on recommendations of previous investigations, we expect a measurable clinical impact.

SAS macro programs for geographically weighted generalized linear modeling with spatial point data: applications to health research

An increasing interest in exploring spatial non-stationarity has generated several specialized analytic software programs; however, few of these programs can be integrated natively into a well-developed statistical environment such as SAS. We not only developed a set of SAS macro programs to fill this gap, but also expanded the geographically weighted generalized linear modeling (GWGLM) by integrating the strengths of SAS into the GWGLM framework. Three features distinguish our work. First, the macro programs of this study provide more kernel weighting functions than the existing programs. Second, with our codes the users are able to better specify the bandwidth selection process compared to the capabilities of existing programs. Third, the development of the macro programs is fully embedded in the SAS environment, providing great potential for future exploration of complicated spatially varying coefficient models in other disciplines. We provided three empirical examples to illustrate the use of the SAS macro programs and demonstrated the advantages explained above.

Geoadditive models to assess spatial variation of HIV infections among women in local communities of Durban, South Africa

BACKGROUND

The severity of the HIV/AIDS epidemic in South Africa varies between and within provinces, with differences noted even at the suburban scale. We investigated the geographical variability of HIV infection in rural areas of the eThekwini Metropolitan Municipality in KwaZulu-Natal province, South Africa.

METHOD

We used geoadditive models to assess nonlinear geographical variation in HIV prevalence while simultaneously controlling for important demographic and sexual risk factors. A total of 3,469 women who were screened for a Phase-III randomized trial were included in the current analysis.

RESULTS

We found significant spatial patterns that could not be explained by demographic and sexual risk behaviors. In particular, the epidemic was determined to be much worse 44 km south of Durban after controlling for all demographic and sexual risk behaviors.

CONCLUSION

The study revealed significant geographic variability in HIV infection in the eThekwini Metropolitan Municipality in KwaZulu-Natal, South Africa.

Spatio-temporal evaluation of cattle trade in Sweden: description of a grid network visualization technique

Understanding the intensity and spatial patterns of animal transfers is of prime importance as geographical moves play an important part in the spread and potential control of contagious animal diseases of veterinary importance. For the purpose of visualizing all registered between-herd animal movements in Sweden between 1 July 2005 and 31 December 2008 by map animation, a grid network technique based on the Bresenham line algorithm was developed. Potential spatio-temporal clustering of animals registered as sold or purchased based on location and month of trade was also detected and tested using a spatial scan statistic. Calculations were based on data from 31,375 holdings and 3,487,426 head of cattle. In total, 988,167 between-herd movements of individual bovines were displayed in a sequence of maps covering three and a half years by 2-week intervals. The maps showed that several cattle movements, both short- and long-distance, take place in Sweden each week of the year. However, most animals (75%) were only registered at one single holding during the study period and 23% were sold to a different holding once. Spatial scan statistics based on data from the year 2008 indicated uneven distributions of purchased or sold animals in space and time. During each autumn, there was an increase in cattle movements and October and November showed significantly more cases of sold or purchased animals (relative risk ~1.7, p = 0.001). Based on the results, we conclude that cattle trade is constantly active at a considerable level. This, in combination with possibly insufficient biosecurity routines applied on many farms, constitutes a risk that contagious diseases are spread in the population. The grid network maps were generated through the use of open-source tools and software in order to decrease software costs and facilitate sharing of programme code. In addition, the technique was based on scripts that allow for the inclusion of iterative processes and that comprise all main parts of map creation. Thereby, a large number of maps can be generated and the demands for high reproducibility are met.

Exposure to particulate air pollution at different living locations and respiratory symptoms in Hong Kong--an application of satellite information

Respiratory ill-health effects due to particulate air exposure at different geographical locations in Hong Kong that aggregate individual living locations were estimated based on satellite information. We assessed the presence of respiratory symptoms of a frequent cough or sputum in school students aged 11-20 years old (n = 9,881). Daily particulate air pollution levels at students' living locations were derived from the surface extinction coefficients measured by satellite and measurements from the air pollutant monitoring stations at ground level. Adjusted odds ratio (OR) [95% CI] of respiratory symptoms was 1.047 [1.005, 1.091] per 10 microg m(-3) increase in PM(10) concentration. Specificity tests showed that adjusted OR of having other symptoms is not significant (p = 0.20-0.94). Exposures to PM(10) at different geographical locations is associated with increased odds of having respiratory symptoms (cough or sputum) but not with other symptoms unrelated to air pollution.

Quantifying aggregated uncertainty in Plasmodium falciparum malaria prevalence and populations at risk via efficient space-time geostatistical joint simulation

Risk maps estimating the spatial distribution of infectious diseases are required to guide public health policy from local to global scales. The advent of model-based geostatistics (MBG) has allowed these maps to be generated in a formal statistical framework, providing robust metrics of map uncertainty that enhances their utility for decision-makers. In many settings, decision-makers require spatially aggregated measures over large regions such as the mean prevalence within a country or administrative region, or national populations living under different levels of risk. Existing MBG mapping approaches provide suitable metrics of local uncertainty—the fidelity of predictions at each mapped pixel—but have not been adapted for measuring uncertainty over large areas, due largely to a series of fundamental computational constraints. Here the authors present a new efficient approximating algorithm that can generate for the first time the necessary joint simulation of prevalence values across the very large prediction spaces needed for global scale mapping. This new approach is implemented in conjunction with an established model for P. falciparum allowing robust estimates of mean prevalence at any specified level of spatial aggregation. The model is used to provide estimates of national populations at risk under three policy-relevant prevalence thresholds, along with accompanying model-based measures of uncertainty. By overcoming previously unchallenged computational barriers, this study illustrates how MBG approaches, already at the forefront of infectious disease mapping, can be extended to provide large-scale aggregate measures appropriate for decision-makers.

Reliable disease maps can support rational decision making. These maps are often made by interpolation: taking disease data from field studies and predicting values for the gaps between the data to make a complete map. Such maps always contain uncertainty, however, and measuring this uncertainty is vital so that the reliability of risk maps can be determined. A modern approach called model-based geostatistics (MBG) has led to increasingly sophisticated ways of mapping disease and measuring spatial uncertainty. Many health management decisions are made for administrative areas (e.g., districts, provinces, countries) and disease maps can support these decisions by averaging their values over the regions of interest. Carrying out this aggregation in conjunction with MBG techniques has not previously been possible for very large maps, however, due largely to the computational constraints involved. This study has addressed this problem by developing a new algorithm and allows aggregation of a global MBG disease map over very large areas. It is used to estimate Plasmodium falciparum malaria prevalence and corresponding populations at risk worldwide, aggregated across regions of different sizes. These estimates are a cornerstone for disease burden estimation and are provided in full to facilitate that process.

Improving geocoding outcomes for the Nebraska Cancer Registry: learning from proven practices

This report summarizes geocoding improvement experiments in the Nebraska Cancer Registry. An initial assessment of previous geocoding suggests that some proven geocoding procedures have not been followed, and overall results were unacceptable. This study concluded that when updating different address files from different time periods, it is sufficient to use the most recent street centerline database. The combination of match score of 80 and spelling sensitivity of 80 in ESRI's ArcGIS geocoder is sufficient for most geocoding purposes. Given the sizable number of unmatched addresses, the Google Maps geocoding service was used. A comparison of 1500 high-quality addresses that were matched by both Google Maps and ArcGIS geocoders shows that, in most cases, the location discrepancies between the two were acceptable. The median distance between each pair of 1500 coded locations was 36.6 meters, with an average of 92.8 meters. Distance discrepancies were larger in urban fringe areas and smaller toward urban centers. It was concluded that by strictly following proven procedures including address coding specification, Internet-based White Pages for reverse address finding, and Internet-based geocoding, a 90% or even a 95% match rate is achievable.

Spatiotemporal antibiotic resistance pattern monitoring using geographical information system based hierarchical cluster analysis

Bacterial antimicrobial resistance in both the medical and agricultural fields has become a serious problem worldwide. Antibiotic resistant strains of bacteria are an increasing threat to human health, with resistance mechanisms having been described to all known antimicrobials currently available for clinical use. Monitoring the geotemporal variations of antibiotic resistance pattern is crucial factor in planning a successful therapeutic guidelines preventing further emergence of antibiotic resistance. This study is based on the retrospective spatiotemporal analysis of laboratory results of Antibiotic Sensitivity Tests, time stamped with the date and time of the microbiological specimen dispatched to the laboratory. Geographic location of the isolated bacterial colony is specified with the latitude and the longitude of the patient's location. Agglomerative Hierarchical Clustering was performed on antimicrobial resistance findings based on the geographic locations generating series of Heatmaps to visualize the extent of the resistance pattern. Sequential Hierarchical cluster analysis was proven to be effective in visualization of antibiotic resistance using Heatmaps demonstrating the temporal variations of the antibiotic resistance patterns.

Obesity atlas and methodbox: towards an open framework for sharing public health intelligence workflows

The large growth in data sources relevant to public health has not been matched by a growth in human resource for producing intelligence to support decisions or generate new insights. There is a need to bring scarce public health expertise into closer alignment with data and data processing methods to support timely public health analysis. The difficulties of developing and sharing this expertise in large organisations such as the UK's National Health Service have long been recognised. We report findings in this area across two projects Obesity Atlas and Methodbox, which are developing and sharing best practice between Public Health Analysts in England, and we address the relevant generic knowledge management problems in the Public Health community.

Satellite imaging and vector-borne diseases: the approach of the French National Space Agency (CNES)

Tele-epidemiology consists in studying human and animal epidemic, the spread of which is closely tied to environmental factors, using data from earth-orbiting satellites. By combining various data originated from satellites such as SPOT (vegetation indexes), Meteosat (winds and cloud masses) and other Earth observation data from Topex/Poseidon and Envisat (wave height, ocean temperature and colour) with hydrology data (number and distribution of lakes, water levels in rivers and reservoirs) and clinical data from humans and animals (clinical cases and serum use), predictive mathematical models can be constructed. A number of such approaches have been tested in the last three years. In Senegal, for example, Rift Valley fever epidemics are being monitored using a predictive model based on the rate at which water holes dry out after the rainy season, which affects the number of mosquito eggs which carry the virus.

A preliminary radon map for Canada according to health region

The recent publications of the combined analyses of residential radon studies in Europe and North America have shown that there is a significant risk of lung cancer at residential radon levels. In order to assess the population risk due to radon, the knowledge of the spatial distribution of indoor radon levels is essential. Here a preliminary radon map for Canada is presented, based on historical radon measurements collected in 6016 locations across Canada with the health region as the basic geographic units.

Plague and the human flea, Tanzania

Pulex irritans fleas were more common in villages with high plague incidence.

Domestic fleas were collected in 12 villages in the western Usambara Mountains in Tanzania. Of these, 7 are considered villages with high plague frequency, where human plague was recorded during at least 6 of the 17 plague seasons between 1986 and 2004. In the remaining 5 villages with low plague frequency, plague was either rare or unrecorded. Pulex irritans, known as the human flea, was the predominant flea species (72.4%) in houses. The density of P. irritans, but not of other domestic fleas, was significantly higher in villages with a higher plague frequency or incidence. Moreover, the P. irritans index was strongly positively correlated with plague frequency and with the logarithmically transformed plague incidence. These observations suggest that in Lushoto District human fleas may play a role in plague epidemiology. These findings are of immediate public health relevance because they provide an indicator that can be surveyed to assess the risk for plague.

Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review

Epidemiologists are adopting new remote sensing techniques to study a variety of vector-borne diseases. Associations between satellite-derived environmental variables such as temperature, humidity, and land cover type and vector density are used to identify and characterize vector habitats. The convergence of factors such as the availability of multi-temporal satellite data and georeferenced epidemiological data, collaboration between remote sensing scientists and biologists, and the availability of sophisticated, statistical geographic information system and image processing algorithms in a desktop environment creates a fertile research environment. The use of remote sensing techniques to map vector-borne diseases has evolved significantly over the past 25 years. In this paper, we review the status of remote sensing studies of arthropod vector-borne diseases due to mosquitoes, ticks, blackflies, tsetse flies, and sandflies, which are responsible for the majority of vector-borne diseases in the world. Examples of simple image classification techniques that associate land use and land cover types with vector habitats, as well as complex statistical models that link satellite-derived multi-temporal meteorological observations with vector biology and abundance, are discussed here. Future improvements in remote sensing applications in epidemiology are also discussed.

[Estimate of HIV prevalence in pregnant women by means of spatial analysis in Southern Brazil]

Spatial analysis techniques were used to estimate the interurban differential HIV prevalence among pregnant women in the city of Porto Alegre, Southern Brazil. The estimates were produced through the spatial smoothing of residence pinpoints with live newborns and HIV infected pregnant women for the year of 2003. The overlay of high prevalence areas in city slums was identified. This finding confirms the intensification of AIDS epidemic among poor urban populations, and indicates areas where basic care and educational strategies should be reinforced.

Participatory mapping of target areas to enable operational larval source management to suppress malaria vector mosquitoes in Dar es Salaam, Tanzania

Background

Half of the population of Africa will soon live in towns and cities where it can be protected from malaria by controlling aquatic stages of mosquitoes. Rigorous but affordable and scaleable methods for mapping and managing mosquito habitats are required to enable effective larval control in urban Africa.

Methods

A simple community-based mapping procedure that requires no electronic devices in the field was developed to facilitate routine larval surveillance in Dar es Salaam, Tanzania. The mapping procedure included (1) community-based development of sketch maps and (2) verification of sketch maps through technical teams using laminated aerial photographs in the field which were later digitized and analysed using Geographical Information Systems (GIS).

Results

Three urban wards of Dar es Salaam were comprehensively mapped, covering an area of 16.8 km². Over thirty percent of this area were not included in preliminary community-based sketch mapping, mostly because they were areas that do not appear on local government residential lists. The use of aerial photographs and basic GIS allowed rapid identification and inclusion of these key areas, as well as more equal distribution of the workload of malaria control field staff.

Conclusion

The procedure developed enables complete coverage of targeted areas with larval control through comprehensive spatial coverage with community-derived sketch maps. The procedure is practical, affordable, and requires minimal technical skills. This approach can be readily integrated into malaria vector control programmes, scaled up to towns and cities all over Tanzania and adapted to urban settings elsewhere in Africa.

The need for the "new health geography" in epidemiologic studies of environment and health

Growth during the past decade in what can be broadly referred to as social and environmental epidemiologic research has been an important contributor to an emerging understanding of environment and health relationships. While the incorporation of geographic information systems as well as concepts such as "neighborhoods" might be viewed as evidence of social epidemiology moving closer to health geography, I argue that the two fields are not well aligned. Health geography has much more to contribute to studies of environment and health, and attention by social epidemiologists to those potential contributions could help rectify this misalignment. This paper suggests a number of geographic perspectives on health and environment that could create useful connections between geography and public health, via social epidemiology. To illustrate this potential, I use an ongoing study of a Texas community exposed to a large petrochemical complex-an inquiry constructed in the mode of social epidemiology - as a case in point. I apply several perspectives and concepts from geography to the case study. Cultural ecology, discourse materialized, political ecology, and territoriality are used to assess the Texas City situation and suggest important types of understandings that can enhance the social epidemiology approach to environment and health. I conclude with a discussion of the prospects for a social epidemiology infused with this type of geographic thought and analysis.

Spatial and temporal variability of the Glossina palpalis palpalis population in the Mbini focus (Equatorial Guinea)

Background

Human African Trypanosomiasis is a vector-borne parasitic disease. The geographical distribution of the disease is linked to the spatial distribution of the tsetse fly. As part of a control campaign using traps, the spatial and temporal variability is analysed of the glossina populations present in the Mbini sleeping sickness foci (Equatorial Guinea).

Results

A significant drop in the annual mean of the G. p. palpalis apparent density was noted from 2004 to 2005, although seasonal differences were not observed. The apparent density (AD) of G. p. palpalis varies significantly from one biotope to another. The fish dryers turned out to be zones with the greatest vector density, although the AD of G. p. palpalis fell significantly in all locations from 2004 to 2005.

Conclusion

Despite the tsetse fly density being relatively low in fish dryers and jetties, the population working in those zones would be more exposed to infection. The mono-pyramidal traps in the Mbini focus have been proven to be a useful tool to control G. p. palpalis, even though the activity on the banks of the River Wele needs to be intensified. The application of spatial analysis techniques and geographical information systems are very useful tools to discriminate zones with high and low apparent density of G. p. palpalis, probably associated with different potential risk of sleeping sickness transmission.

Geographical structures and the cholera epidemic in modern Japan: Fukushima prefecture in 1882 and 1895

BACKGROUND

Disease diffusion patterns can provide clues for understanding geographical change. Fukushima, a rural prefecture in northeast Japan, was chosen for a case study of the late nineteenth century cholera epidemic that occurred in that country. Two volumes of Cholera Ryu-ko Kiji (Cholera Epidemic Report), published by the prefectural government in 1882 and 1895, provide valuable records for analyzing and modelling diffusion. Text descriptions and numerical evidence culled from the reports were incorporated into a temporal-spatial study framework using geographic information system (GIS) and geo-statistical techniques.

RESULTS

Changes in diffusion patterns between 1882 and 1895 reflect improvements in the Fukushima transportation system and growth in social-economic networks. The data reveal different diffusion systems in separate regions in which residents of Fukushima and neighboring prefectures interacted. Our model also shows that an area in the prefecture's northern interior was dominated by a mix of diffusion processes (contagious and hierarchical), that the southern coastal region was affected by a contagious process, and that other infected areas experienced relocation diffusion.

CONCLUSION

In addition to enhancing our understanding of epidemics, the spatial-temporal patterns of cholera diffusion offer opportunities for studying regional change in modern Japan. By highlighting the dynamics of regional reorganization, our findings can be used to better understand the formation of an urban hierarchy in late nineteenth century Japan.

Positional accuracy and geographic bias of four methods of geocoding in epidemiologic research

PURPOSE

We examined the geographic bias of four methods of geocoding addresses using ArcGIS, commercial firm, SAS/GIS, and aerial photography. We compared "point-in-polygon" (ArcGIS, commercial firm, and aerial photography) and the "look-up table" method (SAS/GIS) to allocate addresses to census geography, particularly as it relates to census-based poverty rates.

METHODS

We randomly selected 299 addresses of children treated for asthma at an urban emergency department (1999-2001). The coordinates of the building address side door were obtained by constant offset based on ArcGIS and a commercial firm and true ground location based on aerial photography.

RESULTS

Coordinates were available for 261 addresses across all methods. For 24% to 30% of geocoded road/door coordinates the positional error was 51 meters or greater, which was similar across geocoding methods. The mean bearing was -26.8 degrees for the vector of coordinates based on aerial photography and ArcGIS and 8.5 degrees for the vector based on aerial photography and the commercial firm (p < 0.0001). ArcGIS and the commercial firm performed very well relative to SAS/GIS in terms of allocation to census geography. For 20%, the door location based on aerial photography was assigned to a different block group compared to SAS/GIS. The block group poverty rate varied at least two standard deviations for 6% to 7% of addresses.

CONCLUSION

We found important differences in distance and bearing between geocoding relative to aerial photography. Allocation of locations based on aerial photography to census-based geographic areas could lead to substantial errors.

Match rate and positional accuracy of two geocoding methods for epidemiologic research

PURPOSE

This study compares the match rate and positional accuracy of two geocoding methods: the popular geocoding tool in ArcGIS 9.1 and the Centrus GeoCoder for ArcGIS.

METHODS

We first geocoded 11,016 Texas addresses in a case-control study using both methods and obtained the match rate of each method. We then randomly selected 200 addresses from those geocoded by using both methods and obtained geographic coordinates of the 200 addresses by using a global positioning system (GPS) device. Of the 200 addresses, 110 were case maternal residence addresses and 90 were control maternal residence addresses. These GPS-surveyed coordinates were used as the "true" coordinates to calculate positional errors of geocoded locations. We used Wilcoxon signed rank test to evaluate whether differences in positional errors from the two methods were statistically significantly different from zero. In addition, we calculated the sensitivity and specificity of the two methods for classifying maternal addresses within 1500 m of toxic release inventory facilities when distance is used as a proxy of exposure.

RESULTS

The match rate of the Centrus GeoCoder was more than 10% greater than that of the geocoding tool in ArcGIS 9.1. Positional errors with the Centrus GeoCoder were less than those of the geocoding tool in ArcGIS 9.1, and this difference was statistically significant. Sensitivity and specificity of the two methods are similar.

CONCLUSIONS

Centrus GeoCoder for ArcGIS for geocoding gives greater match rates than the geocoding tool in ArcGIS 9.1. Although the Centrus GeoCoder has better positional accuracy, both methods give similar results in classifying maternal addresses within 1500 m of toxic release inventory facilities when distance is used as a proxy of exposure.

The geographic distribution of melanoma incidence in Massachusetts, adjusted for covariates

Background

The aims of this study were to determine whether observed geographic variations in melanoma cancer incidence in both gender groups are simply random or are statistically significant, whether statistically significant excesses are temporary or persistent, and whether they can be explained by risk factors such as socioeconomic status (SES) or the percent of the population residing in an urban rather than a rural area. Between 1990 and 1999, 4774 female and 5688 male melanomas were diagnosed in Massachusetts residents. Cases were aggregated to census tracts and analyzed for deviations from random occurrence with respect to both spatial location and time.

Results

Thirteen geographic areas that deviated significantly from randomness were uncovered in the age-adjusted analyses of males: five with higher incidence rates than expected and eight lower than expected. In the age-adjusted analyses of females, six areas with higher incidence rates and eight areas with lower than expected incidence rates were found. After adjustment for SES and percent urban, several of these areas were no longer significantly different.

Conclusion

These analyses identify geographic areas with invasive melanoma incidence higher or lower than expected, the times of their excess, and whether or not their status is affected when the model is adjusted for risk factors. These surveillance findings can be a sound starting point for the shoe-leather epidemiologist.

Spatially targeting Culex quinquefasciatus aquatic habitats on modified land cover for implementing an Integrated Vector Management (IVM) program in three villages within the Mwea Rice Scheme, Kenya

BACKGROUND

Continuous land cover modification is an important part of spatial epidemiology because it can help identify environmental factors and Culex mosquitoes associated with arbovirus transmission and thus guide control intervention. The aim of this study was to determine whether remotely sensed data could be used to identify rice-related Culex quinquefasciatus breeding habitats in three rice-villages within the Mwea Rice Scheme, Kenya. We examined whether a land use land cover (LULC) classification based on two scenes, IKONOS at 4 m and Landsat Thematic Mapper at 30 m could be used to map different land uses and rice planted at different times (cohorts), and to infer which LULC change were correlated to high density Cx. quinquefasciatus aquatic habitats. We performed a maximum likelihood unsupervised classification in Erdas Imagine V8.7 and generated three land cover classifications, rice field, fallow and built environment. Differentially corrected global positioning systems (DGPS) ground coordinates of Cx. quinquefasciatus aquatic habitats were overlaid onto the LULC maps generated in ArcInfo 9.1. Grid cells were stratified by levels of irrigation (well-irrigated and poorly-irrigated) and varied according to size of the paddy.

RESULTS

Total LULC change between 1988-2005 was 42.1 % in Kangichiri, 52.8 % in Kiuria and and 50.6 % Rurumi. The most frequent LULC changes was rice field to fallow and fallow to rice field. The proportion of aquatic habitats positive for Culex larvae in LULC change sites was 77.5% in Kangichiri, 72.9% in Kiuria and 73.7% in Rurumi. Poorly - irrigated grid cells displayed 63.3% of aquatic habitats among all LULC change sites.

CONCLUSION

We demonstrate that optical remote sensing can identify rice cultivation LULC sites associated with high Culex oviposition. We argue that the regions of higher Culex abundance based on oviposition surveillance sites reflect underlying differences in abundance of larval habitats which is where limited control resources could be concentrated to reduce vector larval abundance.

Bayesian spatial analysis and disease mapping: tools to enhance planning and implementation of a schistosomiasis control programme in Tanzania

OBJECTIVE

To predict the spatial distributions of Schistosoma haematobium and S. mansoni infections to assist planning the implementation of mass distribution of praziquantel as part of an on-going national control programme in Tanzania.

METHODS

Bayesian geostatistical models were developed using parasitological data from 143 schools.

RESULTS

In the S. haematobium models, although land surface temperature and rainfall were significant predictors of prevalence, they became non-significant when spatial correlation was taken into account. In the S. mansoni models, distance to water bodies and annual minimum temperature were significant predictors, even when adjusting for spatial correlation. Spatial correlation occurred over greater distances for S. haematobium than for S. mansoni. Uncertainties in predictions were examined to identify areas requiring further data collection before programme implementation.

CONCLUSION

Bayesian geostatistical analysis is a powerful and statistically robust tool for identifying high prevalence areas in a heterogeneous and imperfectly known environment.

Basic mapping principles for visualizing cancer data using Geographic Information Systems (GIS)

Maps and other data graphics may play a role in generating ideas and hypotheses at the beginning of a project. They are useful as part of analyses for evaluating model results and then at the end of a project when researchers present their results and conclusions to varied audiences, such as their local research group, decision makers, or a concerned public. Cancer researchers are gaining skill with geographic information system (GIS) mapping as one of their many tools and are broadening the symbolization approaches they use for investigating and illustrating their data. A single map is one of many possible representations of the data, so making multiple maps is often part of a complete mapping effort. Symbol types, color choices, and data classing each affect the information revealed by a map and are best tailored to the specific characteristics of data. Related data can be examined in series with coordinated classing and can also be compared using multivariate symbols that build on the basic rules of symbol design. Informative legend wording and setting suitable map projections are also basic to skilled mapmaking.

Topographic angiography and optical coherence tomography: a correlation of imaging characteristics

PURPOSE

Topographic angiography (TAG) using confocal scanning laser angiography and optical coherence tomography (OCT) are new imaging modalities that have been introduced during recent years. OCT and TAG imaging were compared to specify the characteristics of each imaging modality.

METHODS

TAG using fluorescein angiography (FA) provides a three-dimensional profile of the vascular structures based on the analysis of a set of 32 confocal images over a depth of 4 mm. OCT provides cross-sectional images of the neurosensory retina and the retinal pigment epithelium-choriocapillary complex (RPE-CC). The authors compared and evaluated both modalities in 10 patients with predominantly classic choroidal neovascularization (CNV), 10 patients with serous pigment epithelial detachment (PED), and 10 patients with geographic RPE atrophy, all secondary to age-related macular degeneration (ARMD).

RESULTS

In patients with classic CNV, TAG detected neovascular structures and delineated their configuration. In PEDs pooling of extravascular fluid is demonstrated, and in geographic RPE atrophy TAG showed reduced choroidal perfusion. Classic CNV was demonstrated by OCT as a hyperreflective band at the level of the RPE-CC, and PED showed a dome-shaped RPE detachment. In geographic RPE atrophy, OCT imaged loss of the RPE band and had an increased depth resolution.

CONCLUSIONS

TAG and OCT are useful imaging modalities in the evaluation of ARMD cases. TAG visualizes the vascular configuration and dynamic perfusion and leakage changes. OCT is able to document intra-, subretinal, and sub-RPE fluid accumulation secondary to CNV. Both modalities may provide further valuable insight into ARMD pathogenesis, enhance diagnostic quality, and improve the assessment of therapeutic effects.

Epidemiological geomatics in evaluation of mine risk education in Afghanistan: introducing population weighted raster maps

Evaluation of mine risk education in Afghanistan used population weighted raster maps as an evaluation tool to assess mine education performance, coverage and costs. A stratified last-stage random cluster sample produced representative data on mine risk and exposure to education. Clusters were weighted by the population they represented, rather than the land area. A "friction surface" hooked the population weight into interpolation of cluster-specific indicators. The resulting population weighted raster contours offer a model of the population effects of landmine risks and risk education. Five indicator levels ordered the evidence from simple description of the population-weighted indicators (level 0), through risk analysis (levels 1-3) to modelling programme investment and local variations (level 4). Using graphic overlay techniques, it was possible to metamorphose the map, portraying the prediction of what might happen over time, based on the causality models developed in the epidemiological analysis. Based on a lattice of local site-specific predictions, each cluster being a small universe, the "average" prediction was immediately interpretable without losing the spatial complexity.

Use of GIS in epidemiology: a case study in Istanbul

In this study, the procedure of using GIS for tracking the distribution of measles in a district of Istanbul was performed. The aim of the study is to present the available questionnaire data, which were collected during the measles epidemic, by means of maps. The designed maps show the relation and the distribution of individual cases on time and spatiality. A database is designed according to the questionnaires. Geo-spatial distribution of measles cases was analyzed. The obtained results were discussed and presented.

Rural cases of equine West Nile virus encephalomyelitis and the normalized difference vegetation index

Data from an outbreak (August to October, 2002) of West Nile virus (WNV) encephalomyelitis in a population of horses located in northern Indiana was scanned for clusters in time and space. One significant (p = 0.04) cluster of case premises was detected, occurring between September 4 and 10 in the south-west part of the study area (85.70 degrees N, 45.50 degrees W). It included 10 case premises (3.67 case premises expected) within a radius of 2264 m. Image data were acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensor onboard a National Oceanic and Atmospheric Administration polar-orbiting satellite. The Normalized Difference Vegetation Index (NDVI) was calculated from visible and near-infrared data of daily observations, which were composited to produce a weekly-1km(2) resolution raster image product. During the epidemic, a significant (p < 0.01) decrease (0.025 per week) in estimated NDVI was observed at all case and control premise sites. The median estimated NDVI (0.659) for case premises within the cluster identified was significantly (p < 0.01) greater than the median estimated NDVI for other case (0.571) and control (0.596) premises during the same period. The difference in median estimated NDVI for case premises within this cluster, compared to cases not included in this cluster, was greatest (5.3% and 5.1%, respectively) at 1 and 5 weeks preceding occurrence of the cluster. The NDVI may be useful for identifying foci of WNV transmission.

Climatic, high tide and vector variables and the transmission of Ross River virus

This report assesses the impact of the variability in environmental and vector factors on the transmission of Ross River virus (RRV) in Brisbane, Australia. Poisson time series regression analyses were conducted using monthly data on the counts of RRV cases, climate variables (Southern Oscillation Index and rainfall), high tides and mosquito density for the period of 1998-2001. The results indicate that increases in the high tide (relative risk (RR): 1.65; 95% confidence interval (CI): 1.20-2.26), rainfall (RR: 1.45; 95% CI: 1.21-1.73), mosquito density (RR: 1.17; 95% CI: 1.09-1.27), the density of Culex annulirostris (RR: 1.25; 95% CI: 1.13-1.37) and the density of Ochlerotatus vigilax (RR: 2.39; 95% CI: 2.30-2.48), each at a lag of 1 month, were statistically significantly associated with the rise of monthly RRV incidence. The results of the present study might facilitate the development of early warning systems for reducing the incidence of this wide-spread disease in Australia and other Pacific island nations.

The accuracy of human population maps for public health application

OBJECTIVES

Human population totals are used for generating burden of disease estimates at global, continental and national scales to help guide priority setting in international health financing. These exercises should be aware of the accuracy of the demographic information used.

METHODS

The analysis presented in this paper tests the accuracy of five large-area, public-domain human population distribution data maps against high spatial resolution population census data enumerated in Kenya in 1999. We illustrate the epidemiological significance, by assessing the impact of using these different human population surfaces in determining populations at risk of various levels of climate suitability for malaria transmission. We also describe how areal weighting, pycnophylactic interpolation and accessibility potential interpolation techniques can be used to generate novel human population distribution surfaces from local census information and evaluate to what accuracy this can be achieved.

RESULTS

We demonstrate which human population distribution surface performed best and which population interpolation techniques generated the most accurate bespoke distributions. Despite various levels of modelling complexity, the accuracy achieved by the different surfaces was primarily determined by the spatial resolution of the input population data. The simplest technique of areal weighting performed best.

CONCLUSIONS

Differences in estimates of populations at risk of malaria in Kenya of over 1 million persons can be generated by the choice of surface, highlighting the importance of these considerations in deriving per capita health metrics in public health. Despite focussing on Kenya the results of these analyses have general application and are discussed in this wider context.

Towards empirical description of malaria seasonality in southern Africa: the example of Zimbabwe

BACKGROUND

Quantitative description and mapping of malaria seasonality is important for timely spatial targeting of interventions and for modelling malaria risk. There is a need for seasonality models that predict quantitative variation in transmission between months.

METHODS

We use Zimbabwe as an example for developing an empirical map of malaria seasonality. We describe the relationship between seasonality in malaria and environmental covariates for the period 1988--1999, by fitting a spatial-temporal regression model within a Bayesian framework to provide smoothed maps of the seasonal trend. We adapt a seasonality concentration index used previously for rainfall to quantify malaria case load during the peak transmission season based on monthly values.

RESULTS

Combinations of mean monthly temperature (range 28--32 degrees C), maximum temperature (24--28 degrees C) and high rainfall provide suitable conditions for seasonal transmission. High monthly maximum and mean monthly minimum temperatures limit months of high transmission. The intensity of seasonal transmission was highest in the north western part of the country from February to May with the peak in April and lowest in the whole country from July to December. The north western lowlands had the highest concentration of malaria cases (>25%) followed by some districts in the north central and eastern part with a moderate concentration of cases (20-25%). The central highlands and south eastern part of the country had the lowest concentration of malaria cases (<20%). This pattern was closely associated to the geographic variation in the seasonality of climatic covariates particularly rainfall and temperature. Conclusions Our modelling approach quantifies the geographical variation in seasonal trend and the concentration of cases during the peak transmission season and therefore has potential application in malaria control. The use of a covariate adjusted empirical model may prove useful for predicting the seasonal risk pattern across southern Africa.

[A process and the prospects for the medical geographic study]

Medical geography is an edge subject crossing many other subjects. It has lately become the focus of attention for the continuously worsening of the living surroundings (earth environment) of the human beings and a series of threats it imposes on human health. This text consists of a comprehensive description of the history of medical geography, a detailed introduction of the research work done in China. What's more, we put forward the idea that disease geography and health geography will become the hotspot in recent study in our country.

Modelling malaria risk in East Africa at high-spatial resolution

OBJECTIVES

Malaria risk maps have re-emerged as an important tool for appropriately targeting the limited resources available for malaria control. In Sub-Saharan Africa empirically derived maps using standardized criteria are few and this paper considers the development of a model of malaria risk for East Africa.

METHODS

Statistical techniques were applied to high spatial resolution remotely sensed, human settlement and land-use data to predict the intensity of malaria transmission as defined according to the childhood parasite ratio (PR) in East Africa. Discriminant analysis was used to train environmental and human settlement predictor variables to distinguish between four classes of PR risk shown to relate to disease outcomes in the region.

RESULTS

Independent empirical estimates of the PR were identified from Kenya, Tanzania and Uganda (n = 330). Surrogate markers of climate recorded on-board earth orbiting satellites, population settlement, elevation and water bodies all contributed significantly to the predictive models of malaria transmission intensity in the sub-region. The accuracy of the model was increased by stratifying East Africa into two ecological zones. In addition, the inclusion of urbanization as a predictor of malaria prevalence, whilst reducing formal accuracy statistics, nevertheless improved the consistency of the predictive map with expert opinion malaria maps. The overall accuracy achieved with ecological zone and urban stratification was 62% with surrogates of precipitation and temperature being among the most discriminating predictors of the PR.

CONCLUSIONS

It is possible to achieve a high degree of predictive accuracy for Plasmodium falciparum parasite prevalence in East Africa using high-spatial resolution environmental data. However, discrepancies were evident from mapped outputs from the models which were largely due to poor coverage of malaria training data and the comparable spatial resolution of predictor data. These deficiencies will only be addressed by more random, intensive small areas studies of empirical estimates of PR.

Advances in satellite remote sensing of pheno-climatic features for epidemiological applications

Geographical Information Systems (GIS) and Remote Sensing (RS) technologies are being used increasingly to study the spatial and temporal patterns of diseases. They can be used to complement conventional ecological monitoring and modelling techniques, and provide a means to portray complex relationships in the ecology of diseases with strong environmental determinants. In particular, satellite technology has been extraordinarily improved during recent years, providing new parameters useful to understand the epidemiology of parasites, such as vegetation indices, land surface temperatures, soil moisture and rainfall indices. In the present review, Normalized Difference Vegetation Index (NDVI) is primarily considered, since it is the index characterizing vegetation that is most used in epidemiological studies. Multi-temporal study of RS data allows collection of bio-climatic information about risk area distribution, along with predictive studies and anticipatory models of diseases, at different geographic scales ranging from global to local. The main physical and technological basis of a mathematical model, effective at different scales, for identification of landscape pheno-climatic features is described in the current paper.

Modeling the distribution of Schistosoma mansoni and host snails in Uganda using satellite sensor data and Geographical Information Systems

The potential value of MODIS satellite sensor data on Normalized Difference Vegetation Index (NDVI) and land surface temperatures (LST) for describing the distribution of the Schistosoma mansoni-"Biomphalaria pfeifferi"/Biomphalaria sudanica parasite-snail system in inland Uganda, were tested by developing annual and seasonal composite models, and iteratively analysing for their relationship with parasite and snail distribution. The dry season composite model predicted an endemic area that produced the best fit with the distribution of schools with > or =5% prevalence. NDVI values of 151-174, day temperatures of 26-36 degrees C, and night temperatures of 15-20 degrees C were used as criteria for the prediction model. Using the same approach with host snail data indicated that most of Uganda is suitable "B. pfeifferi"/B. sudanica habitat, except for possibly the north-eastern region of the country. The parasite, however, appears to be restricted in its distribution in both the north-eastern and the south-western regions of Uganda. The absence of disease in the south-west can not be attributed to the absence of snail hosts. Results suggest a combination of satellite sensor data on temperature and standard climate data on precipitation, as the best ecological determinants of the S. mansoni-"B. pfeifferi"/B. sudanica system. Satellite composite models and logistic regression analysis, suggest low night time temperature as one of the significant factors inhibiting S. mansoni transmission in the south-western highland areas of Uganda. The developed models are, however, unique, representing species-specific ecologic preferences of the S. mansoni-"B. Pfeifferi"/B. sudanica system in inland Uganda. Further validation studies are needed to test the value of the model in other countries in East Africa.

Disease mapping and risk assessment in veterinary parasitology: some case studies

Disease mapping and risk assessment are important tasks in the area of medical and veterinary epidemiology. The development of methods for mapping diseases has progressed considerably in recent years. Geographical Information Systems (GIS), Remote Sensing (RS), and Spatial Analysis represent new tools for the study of epidemiology, and their application to parasitology has become more and more advanced, in particular to study the spatial and temporal patterns of diseases. The present review highlights the usefulness of GIS and RS in veterinary parasitology in order to better know the epidemiology of parasite organisms, causing either snail/arthropod borne diseases or direct transmissible diseases, mostly in small areas with a strong impact by man. It demonstrates the potential of these technologies to serve as effective tools for: data capture, mapping and analysis for the development of descriptive parasitological maps; studying the environmental features that influence the distribution of parasites; predicting parasite occurrence/seasonality based on their environmental requirements and as decision support for disease intervention; and surveillance and monitoring of animal diseases.

Application of Geographical Information Systems and Remote Sensing technologies for assessing and monitoring malaria risk

Despite over 30 years of scientific research, algorithm development and multitudes of publications relating Remote Sensing (RS) information with the spatial and temporal distribution of malaria, it is only in recent years that operational products have been adopted by malaria control decision-makers. The time is ripe for the wealth of research knowledge and products from developed countries be made available to the decision-makers in malarious regions of the globe where this information is urgently needed. This paper reviews the capability of RS to provide useful information for operational malaria early warning systems. It also reviews the requirements for monitoring the major components influencing emergence of malaria and provides examples of applications that have been made. Discussion of the issues that have impeded implementation on a global scale and how those barriers are disappearing with recent economic, technological and political developments are explored; and help pave the way for implementation of an integrated Malaria Early Warning System framework using RS technologies.

Biology-based mapping of vector-borne parasites by Geographic Information Systems and Remote Sensing

Applications of growing degree day-water budget analysis and satellite climatology to vector-borne parasites are reviewed to demonstrate the value of using the unique thermal-hydrological preferences and limits of tolerance of individual parasite-vector systems to define the environmental niche of disease agents in the landscape by modern geospatial analysis methods.

A study of the environmental determinants of malaria and schistosomiasis in the Philippines using Remote Sensing and Geographic Information Systems

Malaria and schistosomiasis are two water-related parasitic diseases affecting millions of people worldwide particularly tropical and subtropical countries. In the Philippines, malaria is found in 72 out of 78 provinces while schistosomiasis is endemic in 24 provinces. The Anopheles mosquito and the Oncomelania snail involved in the transmission of these diseases depend on certain environmental determinants that support mosquito and snail populations. This study, done for the first time in the Philippines, successfully showed how Remote Sensing (RS) and Geographical Information Systems (GIS) can be effectively used in showing how these environmental factors affect the spatial distribution of these two diseases. The study sites, i.e. the municipalities of Asuncion and Kapalong, are known endemic sites for both malaria and schistosomiasis. Georeferenced data enabled visualization of prevalence data in relation to physical maps thus facilitating assessment of disease situation in the two municipalities. RS and GIS data proved that other factors aside from climate influence the epidemiology of the diseases in the two sites. Topography and slope as main physical factors influence the vegetation cover, land use and soil type prevailing in particular areas. In addition, water sources especially irrigation networks differed in various places in the study sites in turn affecting the magnitude and distribution of malaria and schistosomiasis. Significant correlations found between the diseases and the environmental variables formed the basis for development of models to predict the disease prevalence in the two municipalities. Proximity to snail breeding sites and irrigation networks and the highly agricultural nature of the barangays were identified as the most common factors that define the high prevalence areas for schistosomiasis confirming the fact that conditions that support the snail populations will in turn favor the presence of the disease. For malaria, the predictive models included temperature, humidity, soil type, predominance of reproduction brush, presence of cultivated areas, distance from deep wells and distance from conventional water source which are in turn influenced by the factor of elevation.

Sizing up human health through remote sensing: uses and misuses

Following the launch of new satellites, remote sensing (RS) has been increasingly implicated in human health research for thirty years, providing a growing availability of images with higher resolution and spectral ranges. However, the scope of applications, beyond theoretical large potentialities, appears limited both by their technical nature and the models developed. An exhaustive review of RS applications in human health highlights the real implication thus far regarding the diversity and range of health issues, remotely sensed data, processes and interpretations. The place of RS is far under its expected potential, revealing fundamental barriers in its implementation for health applications. The selection of images is done by practical considerations as trivial as price and availability, which are often not relevant to addressing health questions requiring suitable resolutions and spatio-temporal range. The relationships of environmental variables from RS, geospatial data from other sources for health investigations are poorly addressed and usually simplified. A discussion covering the potential of RS for human health is developed here to assist health scientists deal with spatial and temporal dynamics of health, by finding the most relevant data and analysis procedures.

An online operational rainfall-monitoring resource for epidemic malaria early warning systems in Africa

Periodic epidemics of malaria are a major public health problem for many sub-Saharan African countries. Populations in epidemic prone areas have a poorly developed immunity to malaria and the disease remains life threatening to all age groups. The impact of epidemics could be minimized by prediction and improved prevention through timely vector control and deployment of appropriate drugs. Malaria Early Warning Systems are advocated as a means of improving the opportunity for preparedness and timely response.

Rainfall is one of the major factors triggering epidemics in warm semi-arid and desert-fringe areas. Explosive epidemics often occur in these regions after excessive rains and, where these follow periods of drought and poor food security, can be especially severe. Consequently, rainfall monitoring forms one of the essential elements for the development of integrated Malaria Early Warning Systems for sub-Saharan Africa, as outlined by the World Health Organization.

The Roll Back Malaria Technical Resource Network on Prevention and Control of Epidemics recommended that a simple indicator of changes in epidemic risk in regions of marginal transmission, consisting primarily of rainfall anomaly maps, could provide immediate benefit to early warning efforts. In response to these recommendations, the Famine Early Warning Systems Network produced maps that combine information about dekadal rainfall anomalies, and epidemic malaria risk, available via their Africa Data Dissemination Service. These maps were later made available in a format that is directly compatible with HealthMapper, the mapping and surveillance software developed by the WHO's Communicable Disease Surveillance and Response Department. A new monitoring interface has recently been developed at the International Research Institute for Climate Prediction (IRI) that enables the user to gain a more contextual perspective of the current rainfall estimates by comparing them to previous seasons and climatological averages. These resources are available at no cost to the user and are updated on a routine basis.

The production and interpretation of disease mapsA methodological case-study

BACKGROUND

The study of the spatial variation in disease incidence is a vital component of descriptive epidemiology. The production of attractive and informative disease maps complements any formal statistical analyses of spatial variations and often their visual impact will influence the recipient of the information much more than the accompanying statistics. Like any other graphical display, however, a map can both inform and mislead.

METHODS

We describe methodological alternatives and pitfalls for the production of disease maps using data on recent suicides in England and Wales as an illustrative example.

RESULTS

Decisions concerning the methods of mapping of suicide rates, including smoothing, choice of grouping criteria and colouring scheme dramatically influence the resulting map and how it might be interpreted by the reader.

CONCLUSIONS

This study provides clinicians and other non-specialist research workers an insight into the methodological pitfalls of disease mapping and atlas production and should also act as a methodological framework for a critical appraisal of published maps and atlases.

Ecologia da paisagem da hantavirose no Estado do Rio Grande do Sul

Esse trabalho tem como objetivo estudar a ecologia da paisagem das hantaviroses no Rio Grande do Sul através do mapeamento da ocorrência de casos e sua sobreposição a mapas de vegetação e relevo. A maior parte dos casos ocorre na primavera em regiões serranas com vegetação secundária e atividade agrícola.