PMC:1247194 / 42980-44394 JSONTXT

{"project":"2_test","denotations":[{"id":"15198921-11266104-28170908","span":{"begin":233,"end":237},"obj":"11266104"},{"id":"15198921-10189627-28170909","span":{"begin":253,"end":257},"obj":"10189627"},{"id":"15198921-8143596-28170910","span":{"begin":1010,"end":1014},"obj":"8143596"},{"id":"15198921-12766217-28170911","span":{"begin":1391,"end":1395},"obj":"12766217"},{"id":"15198921-1464284-28170912","span":{"begin":1408,"end":1412},"obj":"1464284"}],"text":"GIS have been used to evaluate environmental justice issues, usually by linking information about potential sources of environmental pollutants to census information on sociodemographic characteristics of a population (Perlin et al. 2001; Waller et al. 1999). However, only recently have GIS been used in the design of environmental epidemiology studies. Each example in our article demonstrates that GIS can (and perhaps should) be used in the early planning stages of an environmental epidemiology study to help locate a potential study population with a wide range of exposure. The statistical power of an epidemiologic study and the precision of the risk estimates are optimized when the study population includes adequate numbers of those with both high and low exposures. An example of how GIS have been used to identify a study population with a range of exposures is a feasibility study of childhood leukemia and electromagnetic radiation from power transmission lines in New Jersey (Wartenberg et al. 1993). A GIS was used to identify the population living close to transmission lines and a comparison population farther away. Demographic information was evaluated for both the exposed and unexposed populations to determine potential confounding factors. Other examples include the use of GIS for surveillance and study of lead poisoning from residential exposures (Roberts et al. 2003; Wartenberg 1992)."}

{"project":"MyTest","denotations":[{"id":"15198921-11266104-28170908","span":{"begin":233,"end":237},"obj":"11266104"},{"id":"15198921-10189627-28170909","span":{"begin":253,"end":257},"obj":"10189627"},{"id":"15198921-8143596-28170910","span":{"begin":1010,"end":1014},"obj":"8143596"},{"id":"15198921-12766217-28170911","span":{"begin":1391,"end":1395},"obj":"12766217"},{"id":"15198921-1464284-28170912","span":{"begin":1408,"end":1412},"obj":"1464284"}],"namespaces":[{"prefix":"_base","uri":"https://www.uniprot.org/uniprot/testbase"},{"prefix":"UniProtKB","uri":"https://www.uniprot.org/uniprot/"},{"prefix":"uniprot","uri":"https://www.uniprot.org/uniprotkb/"}],"text":"GIS have been used to evaluate environmental justice issues, usually by linking information about potential sources of environmental pollutants to census information on sociodemographic characteristics of a population (Perlin et al. 2001; Waller et al. 1999). However, only recently have GIS been used in the design of environmental epidemiology studies. Each example in our article demonstrates that GIS can (and perhaps should) be used in the early planning stages of an environmental epidemiology study to help locate a potential study population with a wide range of exposure. The statistical power of an epidemiologic study and the precision of the risk estimates are optimized when the study population includes adequate numbers of those with both high and low exposures. An example of how GIS have been used to identify a study population with a range of exposures is a feasibility study of childhood leukemia and electromagnetic radiation from power transmission lines in New Jersey (Wartenberg et al. 1993). A GIS was used to identify the population living close to transmission lines and a comparison population farther away. Demographic information was evaluated for both the exposed and unexposed populations to determine potential confounding factors. Other examples include the use of GIS for surveillance and study of lead poisoning from residential exposures (Roberts et al. 2003; Wartenberg 1992)."}