Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
The possible elevation of disease rates in the proximity of site-specific environmental hazards is much investigated. Single-site studies are subject to problems of reporting bias and statistical power, and multisite studies to heterogeneity of exposure. Both types of studies usually use concentric circular regions centered on a site as a surrogate for defining the exposed and unexposed populations. This approach does not take into account the actual spatial pattern of toxicant dispersion or the spatial pattern associated with the population, and so much useful information is wasted. We report a kernel density technique to map risk contours for disease, which is not influenced by the coordinates of any putative environmental hazard and which could be married to actual spatial exposure patterns.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1097/01.ede.0000121379.57583.84 | DOI Listing |
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