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: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
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
Objectives: To identify women with low mammography utilization.
Methods: We used Classification Tree Analysis among women aged 42-80 from the 2008 Behavioral Risk Factor Surveillance System (N = 169,427) to identify sub-groups along a continuum of screening.
Results: Women with neither a primary care provider nor health insurance had the lowest utilization (33.9%) and were 2.8% of the sample. Non-smoking women aged 55-80, with a primary care provider, health insurance, and income of $75,000 or more had the highest utilization (90.7%) and comprised 5% of the sample.
Conclusion: As access to primary care providers and health insurance increases with the Affordable Care act, classification tree analyses may help to identify women of high priority for intervention.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4211260 | PMC |
http://dx.doi.org/10.5993/AJHB.38.4.2 | DOI Listing |
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