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 ultimate public health objective is the ability to predict and prevent disease, and not necessarily to identify an exhaustive list of potential disease risk factors. For any important public health outcome with multiple and potentially interrelated risk factors, an improved understanding of the contribution of individual and combinations of modifiable risk factors to the disease burden is essential for formulating an appropriate public health strategy. Partitioning techniques that divide the combined impact of multiple risk factors into exposure-specific components while taking into account the potential interrelations among those components, have been described in the epidemiological literature. In this article, we review and compare the available methods and options for such apportionment and apply them in a more general public health context as a method of selecting and prioritizing coronary heart disease (CHD) prevention strategies.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1111/j.1539-6924.2008.01028.x | DOI Listing |
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