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At least 541 records · Page 30Linked to original sources
PubMed2000

[Archives of environmental health digital maps and their delivery by the Internet].

Geographic Information System is becoming a very important tool for environmental health research. We have produced various digital maps for different scales for different purposes. The burst of E. Coli O-157, distribution of hazadarous chemicals in East Asia, and coastal resin pellets are some examples. It now becomes a problem how to archive these digital maps and how to retrieve these maps according to user's request. We have developed a computer system that can store various types of digital maps and can retrieve them from WWW client machines. The key concept of this archive system is to classify every digital maps according to their region of interest.

Environmental Health↗
PubMed1998

[Archives, photographies, maps for malary in Latina Province].

After an historical introduction about ancient institutional regime of present Littoria/Latina province (until 1870 organized in Naples kingdom and Papal States), this essay is going to a swift analysis of marshes who reigned all over the land from the periphery of Rome to Fondi, when transient sheperds and woodmen were the only human beings of marshy land. So teachers for that unlettered people came into these lands, and so physicians came to fight against malary, first symbiotic enemy of man. So drainages were tried from Roman's epoch to Medieval and Illuministic one. We'll see Popes, feudal ladies and at last drainage trusts, all working to improve human life before the birth of Latina province. New cities and towns were born just during these trials; after the experiences of Angelo Celli, Italian Red Cross and Istituto per il risanamento antimalarico della regione pontina, many laws looked to medical aid for workers in malaric zones (exactly specified in topographic maps). In 1934 the Comitato provinciale antimalarico was introduced all over italian territory with the R.D.n. 1265.

Archives↗
PubMed2002

Geography and geographic information systems in family medicine research.

Understanding spatial relationships between determinants and outcomes of health care is important as the concept of population-based health care gains acceptance. A wide range of tools for understanding these spatial relationships is available to the family medicine researcher through the use of Geographic Information Systems (GIS). The power of GIS lies in its ability to display the spatial distribution of a health-related predictor or outcome. These maps can then be used to either generate or test hypotheses that would not have otherwise occurred to the investigator without visualizing the spatial relationships. The type of GIS application used is dependent on the type of data the researcher has and the research question. The three most common types of data are point or event data, lattice data, and geostatistical data. Point or event data can be displayed using a technique known as geocoding. Lattice data is most commonly displayed as shaded or colored areas where the shading represents rates or counts. Geostatistical data provides counts or numbers at a given location. The analytic techniques used for analyzing spatial data depend on the type of data. Maps tell powerful stories and display relationships that may not be obvious using other techniques.

Environmental Exposure↗
PubMed2002

[How to choose in practice a model to describe the geographic variation of cancer incidence? Example of gastrointestinal cancers from Côte-d'Or].

BACKGROUNDS: In epidemiology, standardized Incidence Ratio (SIR) can have large variance and it is then difficult to distinguish random fluctuations from real spatial variations when describing spatial variations in the rate of cancer. In this context, hierarchical model produce smoothed relative risks estimations helpful for solving this problem. The main advantage of these methods is to combine information of each geographical area with that obtained from prior assumption on the similarity between geographical sub-units. Nevertheless different assumptions produce different geographical maps of incidence of cancer, and the purpose of the present study was the development of a strategy to choose the most satisfactory description of the incidence of digestive cancer in a French department. METHODS: The strategy to choose the most satisfactory geographical map depends on the following criteria: variability between geographical sub-units, auto-correlation, and variability within geographical sub-unit. These criteria have been estimated from observed data for each site of cancer. RESULTS: This strategy was applied to digestive tract cancers diagnosed between 1976 and 1997 in the department of Côte-d'Or, France. High-risk areas were often detected in the urban zone of the department, but without autocorrelation in most cases. CONCLUSION: This strategy permitted to describe cancers in very small areas, avoiding to a large extent the danger of focusing on falsely positive high-risk areas.

Algorithms↗
PubMed2003

[Study on the distribution of malaria in Hainan province through spatial local interpolation technique].

OBJECTIVE: To explore the spatial distribution of malaria in Hainan province. METHODS: Maps of spatial distribution on malaria incidence in Hainan province from 1995 to 1999 were produced, using a spatial local interpolation technique in the ArcGIS 8.1 software. Cross-validation techniques were used to evaluate the fitness of the distribution maps. RESULTS: The spatial distribution of malaria in Hainan province from 1995 to 1999 showed that the incidence of malaria in the southern part of Hainan province were higher than that in the north. In the south, the incidence of malaria in east coastal areas were higher than that of other parts, especially in Wuzhi and Yingge mountains. The prediction error of the cross-validation indicated that the spatial distribution maps of malaria in Hainan had good fitness of malaria incidence of Hainan province. CONCLUSION: Spatial local interpolation technique seemed to be promising in describing the spatial distribution of malaria.

Adult↗
PubMed2003

GIS in EMS.

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Ambulances↗