| pubmed-article:15198920 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:15198920 | lifeskim:mentions | umls-concept:C0014507 | lld:lifeskim |
| pubmed-article:15198920 | lifeskim:mentions | umls-concept:C1428114 | lld:lifeskim |
| pubmed-article:15198920 | lifeskim:mentions | umls-concept:C0805586 | lld:lifeskim |
| pubmed-article:15198920 | lifeskim:mentions | umls-concept:C0016884 | lld:lifeskim |
| pubmed-article:15198920 | lifeskim:mentions | umls-concept:C0521116 | lld:lifeskim |
| pubmed-article:15198920 | lifeskim:mentions | umls-concept:C1292724 | lld:lifeskim |
| pubmed-article:15198920 | pubmed:issue | 9 | lld:pubmed |
| pubmed-article:15198920 | pubmed:dateCreated | 2004-6-16 | lld:pubmed |
| pubmed-article:15198920 | pubmed:abstractText | Spatial epidemiology is the description and analysis of geographic variations in disease with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors. We focus on small-area analyses, encompassing disease mapping, geographic correlation studies, disease clusters, and clustering. Advances in geographic information systems, statistical methodology, and availability of high-resolution, geographically referenced health and environmental quality data have created unprecedented new opportunities to investigate environmental and other factors in explaining local geographic variations in disease. They also present new challenges. Problems include the large random component that may predominate disease rates across small areas. Though this can be dealt with appropriately using Bayesian statistics to provide smooth estimates of disease risks, sensitivity to detect areas at high risk is limited when expected numbers of cases are small. Potential biases and confounding, particularly due to socioeconomic factors, and a detailed understanding of data quality are important. Data errors can result in large apparent disease excess in a locality. Disease cluster reports often arise nonsystematically because of media, physician, or public concern. One ready means of investigating such concerns is the replication of analyses in different areas based on routine data, as is done in the United Kingdom through the Small Area Health Statistics Unit (and increasingly in other European countries, e.g., through the European Health and Environment Information System collaboration). In the future, developments in exposure modeling and mapping, enhanced study designs, and new methods of surveillance of large health databases promise to improve our ability to understand the complex relationships of environment to health. | lld:pubmed |
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| pubmed-article:15198920 | pubmed:language | eng | lld:pubmed |
| pubmed-article:15198920 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:15198920 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:15198920 | pubmed:chemical | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:15198920 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:15198920 | pubmed:month | Jun | lld:pubmed |
| pubmed-article:15198920 | pubmed:issn | 0091-6765 | lld:pubmed |
| pubmed-article:15198920 | pubmed:author | pubmed-author:ElliottPaulP | lld:pubmed |
| pubmed-article:15198920 | pubmed:author | pubmed-author:WartenbergDan... | lld:pubmed |
| pubmed-article:15198920 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:15198920 | pubmed:volume | 112 | lld:pubmed |
| pubmed-article:15198920 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:15198920 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:15198920 | pubmed:pagination | 998-1006 | lld:pubmed |
| pubmed-article:15198920 | pubmed:dateRevised | 2009-11-18 | lld:pubmed |
| pubmed-article:15198920 | pubmed:meshHeading | pubmed-meshheading:15198920... | lld:pubmed |
| pubmed-article:15198920 | pubmed:meshHeading | pubmed-meshheading:15198920... | lld:pubmed |
| pubmed-article:15198920 | pubmed:meshHeading | pubmed-meshheading:15198920... | lld:pubmed |
